research paper about the impact of covid 19 on education

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The Impact of COVID-19 on Education: A Meta-Narrative Review

Aras bozkurt.

1 Distance Education Department, Anadolu University, Eskişehir, Turkey

2 Department of English Studies, University of South Africa, Pretoria, South Africa

3 Anadolu Üniversitesi, Açıköğretim Fakültesi, Kat:7, Oda:702, 26470, Tepebaşı, Eskişehir, Turkey

Kadir Karakaya

4 Applied Linguistics & Technology Department, Iowa State University, Ames, IA USA

5 Educational Psychology, Learning Sciences, University of Oklahoma, Norman, OK USA

Özlem Karakaya

6 Educational Technology & Human-Computer Interaction, Iowa State University, Ames, IA USA

Daniela Castellanos-Reyes

7 Curriculum and Instruction, Learning Design and Technology, Purdue University, West Lafayette, IN USA

Associated Data

The dataset is available from the authors upon request.

The rapid and unexpected onset of the COVID-19 global pandemic has generated a great degree of uncertainty about the future of education and has required teachers and students alike to adapt to a new normal to survive in the new educational ecology. Through this experience of the new educational ecology, educators have learned many lessons, including how to navigate through uncertainty by recognizing their strengths and vulnerabilities. In this context, the aim of this study is to conduct a bibliometric analysis of the publications covering COVID-19 and education to analyze the impact of the pandemic by applying the data mining and analytics techniques of social network analysis and text-mining. From the abstract, title, and keyword analysis of a total of 1150 publications, seven themes were identified: (1) the great reset, (2) shifting educational landscape and emerging educational roles (3) digital pedagogy, (4) emergency remote education, (5) pedagogy of care, (6) social equity, equality, and injustice, and (7) future of education. Moreover, from the citation analysis, two thematic clusters emerged: (1) educational response, emergency remote education affordances, and continuity of education, and (2) psychological impact of COVID-19. The overlap between themes and thematic clusters revealed researchers’ emphasis on guaranteeing continuity of education and supporting the socio-emotional needs of learners. From the results of the study, it is clear that there is a heightened need to develop effective strategies to ensure the continuity of education in the future, and that it is critical to proactively respond to such crises through resilience and flexibility.

Introduction

The Coronavirus (COVID-19) pandemic has proven to be a massive challenge for the entire world, imposing a radical transformation in many areas of life, including education. It was rapid and unexpected; the world was unprepared and hit hard. The virus is highly contagious, having a pathogenic nature whose effects have not been limited to humans alone, but rather, includes every construct and domain of societies, including education. The education system, which has been affected at all levels, has been required to respond to the crisis, forced to transition into emergency modes, and adapt to the unprecedented impact of the global crisis. Although the beginning of 2021 will mark nearly a year of experience in living through the pandemic, the crisis remains a phenomenon with many unknowns. A deeper and more comprehensive understanding of the changes that have been made in response to the crisis is needed to survive in these hard times. Hence, this study aims to provide a better understanding by examining the scholarly publications on COVID-19 and education. In doing this, we can identify our weaknesses and vulnerabilities, be better prepared for the new normal, and be more fit to survive.

Related Literature

Though the COVID-19 pandemic is not the first major disruption to be experienced in the history of the world, it has been unique due to its scale and the requirements that have been imposed because of it (Guitton, 2020 ). The economies of many countries have greatly suffered from the lockdowns and other restrictive measurements, and people have had to adapt to a new lifestyle, where their primary concern is to survive by keeping themselves safe from contracting the deadly virus. The education system has not been exempt from this series of unfortunate events inflicted by COVID-19. Since brick-and-mortar schools had to be closed due to the pandemic, millions of students, from those in K-12 to those in higher education, were deprived of physical access to their classrooms, peers, and teachers (Bozkurt & Sharma, 2020a , b ). This extraordinary pandemic period has posed arguably the most challenging and complex problems ever for educators, students, schools, educational institutions, parents, governments, and all other educational stakeholders. The closing of brick-and-mortar schools and campuses rendered online teaching and learning the only viable solution to the problem of access-to-education during this emergency period (Hodges et al., 2020 ). Due to the urgency of this move, teachers and instructors were rushed to shift all their face-to-face instruction and instructional materials to online spaces, such as learning management systems or electronic platforms, in order to facilitate teaching virtually at a distance. As a result of this sudden migration to learning and instruction online, the key distinctions between online education and education delivered online during such crisis and emergency circumstances have been obfuscated (Hodges et al., 2020 ).

State of the Current Relevant Literature

Although the scale of the impact of the COVID-19 global pandemic on education overshadows previously experienced nationwide or global crises or disruptions, the phenomenon of schools and higher education institutions having to shift their instruction to online spaces is not totally new to the education community and academia (Johnson et al., 2020 ). Prior literature on this subject indicates that in the past, schools and institutions resorted to online or electronic delivery of instruction in times of serious crises and uncertainties, including but not limited to natural disasters such as floods or earthquakes (e.g., Ayebi-Arthur, 2017 ; Lorenzo, 2008 ; Tull et al., 2017 ), local disruptions such as civil wars and socio-economic events such as political upheavals, social turmoils or economic recessions (e.g., Czerniewicz et al., 2019 ). Nevertheless, the past attempts to move learning and teaching online do not compare to the current efforts that have been implemented during the global COVID-19 pandemic, insofar as the past crisis situations were sporadic events in specific territories, affecting a limited population for relatively short periods of time. In contrast, the COVID-19 pandemic has continued to pose a serious threat to the continuity of education around the globe (Johnson et al., 2020 ).

Considering the scale and severity of the global pandemic, the impacts it has had on education in general and higher education in particular need to be explored and studied empirically so that necessary plans and strategies aimed at reducing its devastating effects can be developed and implemented. Due to the rapid onset and spread of the global pandemic, the current literature on the impact of COVID-19 on education is still limited, including mostly non-academic editorials or non-empirical personal reflections, anecdotes, reports, and stories (e.g., Baker, 2020 ; DePietro, 2020 ). Yet, with that said, empirical research on the impact of the global pandemic on higher education is rapidly growing. For example, Johnson et al. ( 2020 ), in their empirical study, found that faculty members who were struggling with various challenges adopted new instructional methods and strategies and adjusted certain course components to foster emergency remote education (ERE). Unger and Meiran ( 2020 ) observed that the pandemic made students in the US feel anxious about completing online learning tasks. In contrast, Suleri ( 2020 ) reported that a large majority of European higher education students were satisfied with their virtual learning experiences during the pandemic, and that most were willing to continue virtual higher education even after the pandemic (Suleri, 2020 ). The limited empirical research also points to the need for systematically planning and designing online learning experiences in advance in preparation for future outbreaks of such global pandemics and other crises (e.g., Korkmaz & Toraman, 2020 ). Despite the growing literature, the studies provide only fragmentary evidence on the impact of the pandemic on online learning and teaching. For a more thorough understanding of the serious implications the pandemic has for higher education in relation to learning and teaching online, more empirical research is needed.

Unlike previously conducted bibliometric analysis studies on this subject, which have largely involved general analysis of research on health sciences and COVID-19, Aristovnik et al. ( 2020 ) performed an in-depth bibliometric analysis of various science and social science research disciplines by examining a comprehensive database of document and source information. By the final phase of their bibliometric analysis, the authors had analyzed 16,866 documents. They utilized a mix of innovative bibliometric approaches to capture the existing research and assess the state of COVID-19 research across different research landscapes (e.g., health sciences, life sciences, physical sciences, social sciences, and humanities). Their findings showed that most COVID-19 research has been performed in the field of health sciences, followed by life sciences, physical sciences, and social sciences and humanities. Results from the keyword co-occurrence analysis revealed that health sciences research on COVID-19 tended to focus on health consequences, whereas the life sciences research on the subject tended to focus on drug efficiency. Moreover, physical sciences research tended to focus on environmental consequences, and social sciences and humanities research was largely oriented towards socio-economic consequences.

Similarly, Rodrigues et al. ( 2020 ) carried out a bibliometric analysis of COVID-19 related studies from a management perspective in order to elucidate how scientific research and education arrive at solutions to the pandemic crisis and the post-COVID-19 era. In line with Aristovnik et al.’s ( 2020 ) findings, Rodrigues et al. ( 2020 ) reported that most of the published research on this subject has fallen under the field of health sciences, leaving education as an under-researched area of inquiry. The content analysis they performed in their study also found a special emphasis on qualitative research. The descriptive and content analysis yielded two major strands of studies: (1) online education and (2) COVID-19 and education, business, economics, and management. The online education strand focused on the issue of technological anxiety caused by online classes, the feeling of belonging to an academic community, and feedback.

Lastly, Bond ( 2020 ) conducted a rapid review of K-12 research undertaken in the first seven months of the COVID-19 pandemic to identify successes and challenges and to offer recommendations for the future. From a search of K-12 research on the Web of Science, Scopus, EBSCOHost, the Microsoft Academic, and the COVID-19 living systematic map, 90 studies were identified and analyzed. The findings revealed that the reviewed research has focused predominantly on the challenges to shifting to ERE, teacher digital competencies and digital infrastructure, teacher ICT skills, parent engagement in learning, and students’ health and well-being. The review highlighted the need for straightforward communication between schools and families to inform families about learning activities and to promote interactivity between students. Teachers were also encouraged to develop their professional networks to increase motivation and support amongst themselves and to include opportunities for both synchronous and asynchronous interaction for promoting student engagement when using technology. Bond ( 2020 ) reported that the reviewed studies called for providing teachers with opportunities to further develop their digital technical competencies and their distance and online learning pedagogies. In a recent study that examines the impact of COVID-19 at higher education (Bozkurt, 2022 ), three broad themes from the body of research on this subject: (1) educational crisis and higher education in the new normal: resilience, adaptability, and sustainability, (2) psychological pressures, social uncertainty, and mental well-being of learners, and (3) the rise of online distance education and blended-hybrid modes. The findings of this study are similar to Mishra et al. ( 2021 ) who examined the COVID-19 pandemic from the lens of online distance education and noted that technologies for teaching and learning and psychosocial issues were emerging issues.

The aforementioned studies indicate that a great majority of research on COVID-19 has been produced in the field of health sciences, as expected. These studies nonetheless note that there is a noticeable shortage of studies dealing with the effects of the pandemic in the fields of social sciences, humanities, and education. Given the profound impact of the pandemic on learning and teaching, as well as on the related stakeholders in education, now more than ever, a greater amount of research on COVID-19 needs to be conducted in the field of education. The bibliometric studies discussed above have analyzed COVID-19 research across various fields, yielding a comparative snapshot of the research undertaken so far in different research spheres. However, despite being comprehensive, these studies did not appear to have examined a specific discipline or area of research in depth. Therefore, this bibliometric study aims to provide a focused, in-depth analysis of the COVID-19-related research in the field of education. In this regard, the main purpose of this study is to identify research patterns and trends in the field of education by examining COVID-19-related research papers. The study sought to answer the following research questions:

• What are the thematic patterns in the title, abstract, and keywords of the publications on COVID-19 and education?
• What are the citation trends in the references of the sampled publications on COVID-19 and education?

Methodology

This study used data mining and analytic approaches (Fayyad et al., 2002 ) to examine bibliometric patterns and trends. More specifically, social network analysis (SNA) (Hansen et al., 2020 ) was applied to examine the keywords and references, while text-mining was applied (Aggarwal & Zhai, 2012 ) to examine the titles and abstracts of the research corpus. Keywords represent the essence of an article at a micro level and for the analysis of the keywords, SNA was used. SNA “provides powerful ways to summarize networks and identify key people, [entities], or other objects that occupy strategic locations and positions within a matrix of links” (Hansen et al., 2020 , p. 6). In this regard, the keywords were analyzed based on their co-occurrences and visualized on a network graph by identifying the significant keywords which were demonstrated as nodes and their relationships were demonstrated with ties. For text-mining of the titles and abstracts, the researchers performed a lexical analysis that employs “two stages of co-occurrence information extraction—semantic and relational—using a different algorithm for each stage” (Smith & Humphreys, 2006 , p. 262). Thus, text-mining analysis enabled researchers to identify the hidden patterns and visualize them on a thematic concept map. For the analysis of the references, the researchers further used SNA based on the arguments that “citing articles and cited articles are linked to each other through invisible ties, and they collaboratively and collectively build an intellectual community that can be referred to as a living network, structure, or an ecology” (Bozkurt, 2019 , p. 498). The analysis of the references enabled the researchers to identify pivotal scholarly contributions that guided and shaped the intellectual landscape. The use of multiple approaches enables the study to present a broader view, or a meta-narrative.

Sample and Inclusion Criteria

The publications included in this research met the following inclusion criteria: (1) indexed by the Scopus database, (2) written in English, and (3) had the search queries on their title (Table ​ (Table1). 1 ). The search query reflects the focus on the impact of COVID-19 on education by including common words in the field like learn , teach , or student . Truncation was also used in the search to capture all relevant literature. Narrowing down the search allowed us to exclude publications that were not education related. Scopus was selected because it is one of the largest scholarly databases, and only publications in English were selected to facilitate identification of meaningful lexical patterns through text-mining and provide a condensed view of the research. The search yielded a total of 1150 papers (articles = 887, editorials = 66, notes = 58, conference papers = 56, letters = 40, review studies = 30, book chapters = 9, short surveys = 3, books = 1).

Search strings used to create research corpus

Data Analysis and Research Procedures

This study has two phases of analysis. In the first phase, text mining was used to analyze titles and abstracts, and SNA was applied to analyze keywords. By using two different analytical approaches, the authors were able to triangulate the research findings (Thurmond, 2001 ). In this phase, using lexical algorithms, text mining analysis enabled visualizing the textual data on a thematic concept map according to semantic relationships and co-occurrences of the words (Fig.  1 ). Text mining generated a machine-based concept map by analyzing the co-occurrences and lexical relationships of textual data. Then, based on the co-occurrences and centrality metrics, SNA enabled visualizing keywords on a network graphic called sociogram (Fig.  2 ). SNA allowed researchers to visually identify the key terms on a connected network graph where keywords are represented as nodes and their relationships are represented as edges. In the first phase of the study, by synthesizing outputs of the data mining and analytic approaches, meaningful patterns of textual data were presented as seven main research themes.

Thematic concept mapping of COVID-19 and education-related papers

Social networks analysis of the keywords in COVID-19 and education-related papers

In the second phase of the study, through the examination of the references and citation patterns (e.g., citing and being cited) of the articles in the research corpus, the citation patterns were visualized on a network graphic by clusters (See Fig.  3 ) showing also chronical relationships which enabled to identify pivotal COVID-19 studies. In the second phase of the study, two new themes were identified which were in line with the themes that emerged in the first phase of the study.

Social networks analysis of the references in COVID-19 and education-related papers 2019–2020 (Only the first authors were labeled – See Appendix Fig. ​ Fig.4 4 for SNA of references covering pre-COVID-19 period)

Strengths and Limitations

This study is one of the first attempts to use bibliometric approaches benefiting from data mining and analysis techniques to better understand COVID-19 and its consequences on published educational research. By applying such an approach, a large volume of data is able to be visualized and reported. However, besides these strengths, the study also has certain limitations. First, the study uses the Scopus database, which, though being one of the largest databases, does not include all types of publications. Therefore, the publications selected for this study offer only a partial view, as there are many significant publications in gray literature (e.g., reports, briefs, blogs). Second, the study includes only publications written in English, however, with COVID-19 being a global crisis, publications in different languages would provide a complementary view and be helpful in understanding local reflections in the field of education.

Findings and Discussion

Sna and text-mining: thematic patterns in the title, abstract, and keywords of the publications.

This section reports the findings based on a thematic concept map and network graphic that were developed through text mining (Fig.  1 —Textual data composed of 186.234 words visualized according to lexical relationships and co-occurrences) and sociograms created using SNA (Fig.  2 —The top 200 keywords with highest betweenness centrality and 1577 connections among them mapped on a network graph) to visualize the data. Accordingly, seven major themes were identified by analyzing the data through text-mining and SNA: (1) the great reset, (2) digital pedagogy, (3) shifting educational landscape and emerging educational roles, (4) emergency remote education, (5) pedagogy of care, (6) social equity, equality, and injustice, and (7) future of education.

• Theme 1: The Great Reset (See path Fig.  1 : lockdown  +  emergency  +  community  +  challenges  +  during  >  pandemic and impact  >  outbreak  >  coronavirus  >  pandemic and global  >  crisis  >  pandemic  >  world; See nodes on Fig.  2 : Covid19, pandemic, Coronavirus, lockdown, crisis ). The first theme in the thematic concept map and network graphic is the Great Reset. It has been relatively a short time since the World Health Organization (WHO) declared the COVID-19 a pandemic. Although vaccination had already started, the pandemic continued to have an adverse impact on the world. Ever since the start of the pandemic, people were discussing when there would be a return to normal (Bozkurt & Sharma, 2020a , b ; Xiao, 2021 ); however, as time goes by, this hope has faded, and returning to normal appears to be far into the future (Schwab & Malleret, 2020 ). The pandemic is seen as a major milestone, in the sense that a macro reset in economic, social, geopolitical, environmental, and technological fields will produce multi-faceted changes affecting almost all aspects of life (Schwab & Malleret, 2020 ). The cover of an issue of the international edition of Time Magazine reflected this idea of a great reset and presented the COVID-19 pandemic as an opportunity to transform the way we live and work (Time, 2020 ). It has been argued that the pandemic will generate the emergence of a new era, and that we will have to adapt to the changes it produces (Bozkurt & Sharma, 2020 ). For example, the industrial sector quickly embraced remote work despite its challenges, and it is possible that most industrial companies will not return to the on-site working model even after the pandemic ends (Hern, 2020 ). We can expect a high rate of similar responses in other fields, including education, where COVID-19 has already reshaped our educational systems, the way we deliver education, and pedagogical approaches.
• Theme 2: Digital pedagogy (See path on Fig.  1 : distance learning  >  research  >  teacher  >  development  >  need  >  training  +  technology  +  virtual  >  digital  >  communication  >  support  >  process  >  teaching  >  online  >  learning  >  online learning  +  course  >  faculty  >  students  >  experience ; See nodes on Fig.  2 : online learning, distance learning, computer-based learning, elearning, online education, distance education, online teaching, multimedia-based learning, technology, blended learning, online, digital transformation, ICT, online classes, flexible learning, technology-enhanced learning, digitalization ). Owing to the rapid transition to online education as a result of COVID-19, digital pedagogy and teachers’ competencies in information and communication technology (ICT) integration have gained greater prominence with the unprecedented challenges teachers have faced to adapt to remote teaching and learning. The COVID-19 pandemic has unquestionably manifested the need to prepare teachers to teach online, as most of them have been forced to assume ERE roles with inadequate preparation. Studies involving the use of SNA indicate a correspondence between adapting to a digital pedagogy and the need to equip teachers with greater competency in technology and online teaching (e.g., Blume, 2020 ; König et al., 2020 ). König et al. ( 2020 ) conducted a survey-based study investigating how early career teachers have adapted to online teaching during COVID-19 school closures. Their study found that while all the teachers maintained communication with students and their parents, introduced new learning content, and provided feedback, they lacked the ability to respond to challenges requiring ICT integration, such as those related to providing quality online teaching and to conducting assessments. Likewise, Blume ( 2020 ) noted that most teachers need to acquire digital skills to implement digitally-mediated pedagogy and communication more effectively. Both study findings point to the need for building ICT-related teaching and learning competencies in initial teacher education and teacher professional development. The findings from the SNA conducted in the present study are in line with the aforementioned findings in terms of keyword analysis and overlapping themes and nodes.
• Theme 3: Shifting educational landscape and emerging educational roles (See path on Fig.  1 : future > education > role > Covid19; See nodes on Fig.  2 : higher education, education, student, curriculum, university, teachers, learning, professional development, teacher education, knowledge, readiness ). The role of technology in education and human learning has been essential during the COVID-19 pandemic. Technology has become a prerequisite for learning and teaching during the pandemic and will likely continue to be so after it. In the rapid shift to an unprecedented mode of learning and teaching, stakeholders have had to assume different roles in the educational landscape of the new normal. For example, in a comprehensive study involving the participation of over 30 K higher education students from 62 countries conducted by Aristovnik et al. ( 2020 ), it was found that students with certain socio-demographic characteristics (male, lower living standard, from Africa or Asia) were significantly less satisfied with the changes to work/life balance created by the COVID-19 pandemic, and that female students who were facing financial problems were generally more affected by COVID-19 in their emotional life and personal circumstances. Despite the challenges posed by the pandemic, there is likely to be carry over in the post-pandemic era of some of the educational changes made during the COVID-19 times. For example, traditional lecture-based teacher-centered classes may be replaced by more student-centered online collaborative classes (Zhu & Liu, 2020 ). This may require the development and proliferation of open educational platforms that allow access to high-quality educational materials (Bozkurt et al., 2020 ) and the adoption of new roles to survive in the learning ecologies informed by digital learning pedagogies. In common with the present study, the aforementioned studies (e.g., Aristovnik et al., 2020 ; König et al., 2020 ) call for more deliberate actions to improve teacher education programs by offering training on various teaching approaches, such as blended, hybrid, flexible, and online learning, to better prepare educators for emerging roles in the post-pandemic era.
• Theme 4: Emergency remote education (see path Fig.  1 : higher education  >  university  >  student  >  experience  >  remote; See nodes on Fig.  2 : Covid19, pandemic, Coronavirus, higher education, education, school closure, emergency remote teaching, emergency remote learning ). Educational institutions have undergone a rapid shift to ERE in the wake of COVID-19 (Bozkurt & Sharma, 2020a ; Bozkurt et al., 2020 ; Hodges et al., 2020 ). Although ERE is viewed as similar to distance education, they are essentially different. That is, ERE is a prompt response measure to an emergency situation or unusual circumstances, such as a global pandemic or a civil war, for a temporary period of time, whereas distance education is a planned and systematic approach to instructional design and development grounded in educational theory and practice (Bozkurt & Sharma, 2020b ). Due to the urgent nature of situations requiring ERE, it may fall short in embracing the solid pedagogical learning and teaching principles represented by distance education (Hodges et al., 2020 ). The early implementations of ERE primarily involved synchronous video-conferencing sessions that sought to imitate in-person classroom instruction. It is worth noting that educators may have heavily relied on synchronous communication to overcome certain challenges, such as the lack of available materials and planned activities for asynchronous communication. Lockdowns and school closures, which turned homes into compulsory learning environments, have posed major challenges for families and students, including scheduling, device sharing, and learner engagement in a socially distanced home learning environment (Bond, 2020 ). For example, Shim and Lee ( 2020 ) conducted a qualitative study exploring university students’ ERE experiences and reported that students complained about network instability, unilateral interactions, and reduced levels of concentration. The SNA findings clearly highlight that there has been a focus on ERE due to the school closures during the COVID-19 pandemic. It is key to adopt the best practices of ERE and to utilize them regularly in distance education (Bozkurt, 2022 ). Moreover, it is important to note that unless clear distinctions are drawn between these two different forms of distance education or virtual instruction, a series of unfortunate events in education during these COVID-19 times is very likely to take place and lead to fatal errors in instructional practices and to poor student learning outcomes.
• Theme 5: Pedagogy of care (See path Fig.  1 : r ole  >  education  >  Covid19  >  care ; See nodes on Fig.  2 : Stress, anxiety, student wellbeing, coping, care, crisis management, depression ). The thematic concept map and network graphic show the psychological and emotional impact of the COVID-19 pandemic on various stakeholders, revealing that they have experienced anxiety, expressed the need for care, and sought coping strategies. A study by Baloran ( 2020 ), conducted in the southern part of the Philippines to examine college students’ knowledge, attitudes, anxiety, and personal coping strategies during the COVID-19 pandemic, found that the majority of the students experienced anxiety during the lockdown and worried about food security, financial resources, social contact, and large gatherings. It was reported that the students coped with this anxiety by following protective measures, chatting with family members and friends, and motivating themselves to have a positive attitude. In a similar study, Islam et al. ( 2020 ) conducted an investigation to determine whether Bangladeshi college students experienced anxiety and depression and the factors responsible for these emotional responses. Their cross-sectional survey-based study found that a large percentage of the participants had suffered from anxiety and depression during the pandemic. Academic and professional uncertainty, as well as financial insecurity, have been documented as factors contributing to the anxiety and depression among college students. Both studies point to the need for support mechanisms to be established by higher education institutions in order to ensure student wellbeing, provide them with care, and help them to cope with stress, anxiety, and depression. Talidong and Toquero ( 2020 ) reported that, in addition to students’ well-being and care, teachers’ perceptions and experiences of stress and anxiety during the quarantine period need to be taken into account. The authors found that teachers were worried about the safety of their loved ones and were susceptible to anxiety but tended to follow the preventive policies. A pedagogy of care has been presented as an approach that would effectively allow educators to plan more supportive teaching practices during the pandemic by fostering clear and prompt communication with students and their families and taking into consideration learner needs in lesson planning (e.g., Karakaya, 2021 ; Robinson et al., 2020 ). Here it is important to stress that a pedagogy of care is a multifaceted concept, one that involves the concepts of social equity, equality, and injustice.
• Theme 6: Social equity, equality, and injustice (See path on Fig.  1 : Impact  >  outbreak  >  coronavirus  >  pandemic  >  social ; See nodes on Fig.  2 : Support, equity, social justice, digital divide, inequality, social support ). One of the more significant impacts of COVID-19 has been the deepening of the existing social injustices around the world (Oldekop et al., 2020 ; Williamson et al., 2020 ). Long-term school closures have deteriorated social bonds and adversely affected health issues, poverty, economy, food insecurity, and digital divide (Van Lancker & Parolin, 2020 ). Regarding the digital divide, there has been a major disparity in access to devices and data connectivity between high-income and low-income populations increasing the digital divide, social injustice, and inequality in the world (Bozkurt et al., 2020 ). In line with the SNA findings, the digital divide, manifesting itself most visibly in the inadequacy and insufficiency of digital devices and lack of high-speed Internet, can easily result in widespread inequalities. As such, the disparities between low and high socio-economic status families and school districts in terms of digital pedagogy inequality may deepen as teachers in affluent schools are more likely to offer a wide range of online learning activities and thereby secure better student engagement, participation, and interaction (Greenhow et al., 2020 ). These findings demonstrate that social inequities have been sharpened by the unfortunate disparities imposed by the COVID-19, thus requiring us to reimagine a future that mitigates such concerns.
• Theme 7: Future of education (See word path on Fig.  1 : Future  >  education  >  Covid19  >  pandemic  >  changes and pandemic  >  coronavirus, outbreak, impact  >  world ; See nodes on Fig.  2 : Sustainability, resilience, uncertainty, sdg4). Most significantly, COVID-19 the pandemic has shown the entire world that teachers and schools are invaluable resources and execute critical roles in society. Beyond that, with the compulsory changes resulting from the pandemic, it is evident that teaching and learning environments are not exclusive to brick-and-mortar classrooms. Digital technologies, being at the center of teaching and learning during the pandemic period, have been viewed as a pivotal agent in leveraging how learning takes place beyond the classroom walls (Quilter-Pinner & Ambrose, 2020 ). COVID-19 has made some concerns more visible. For example, the well-being of students, teachers, and society at large has gained more importance in these times of crisis. Furthermore, the need for educational technology and digital devices has compounded and amplified social inequities (Pelletier et al., 2021 ; West & Allen, 2020 ). Despite its global challenges, the need for technology and digital devices has highlighted some advantages that are likely to shape the future of education, particularly those related to the benefits of educational technology. For example, online learning could provide a more flexible, informal, self-paced learning environment for students (Adedoyin & Soykan, 2020 ). However, it also bears the risk of minimizing social interaction, as working in shared office environments has shifted to working alone in home-office settings. In this respect, the transformation of online education must involve a particular emphasis on sustaining interactivity through technology (Dwivedi et al., 2020 ). In view of the findings of the aforementioned studies, our text-mining and SNA findings suggest that the COVID-19 impositions may strongly shape the future of education and how learning takes place.

In summary, these themes extracted from the text-mining and SNA point to a significant milestone in the history of humanity, a multi-faceted reset that will affect many fields of life, from education and economics to sociology and lifestyle. The resulting themes have revealed that our natural response to an emerging worldwide situation shifted the educational landscape. The early response of the educational system was emergency-based and emphasized the continuance of in-person instruction via synchronous learning technologies. The subsequent response foregrounded the significance of digitally mediated learning pedagogy, related teacher competencies, and professional development. As various stakeholders (e.g., students, teachers, parents) have experienced a heightened level of anxiety and stress, an emerging strand of research has highlighted the need for care-based and trauma-informed pedagogies as a response to the side effects of the pandemic. In addition, as the global pandemic has made systemic impairments, such as social injustice and inequity, more visible, an important line of research has emerged on how social justice can be ensured given the challenges caused by the pandemic. Lastly, a sizable amount of research indicates that although the COVID-19 pandemic has imposed unprecedented challenges to our personal, educational, and social lives, it has also taught us how to respond to future crises in a timely, technologically-ready, pedagogically appropriate, and inclusive manner.

SNA: Citation Trends in the References of the Sampled Publications

The trends identified through SNA in citation patterns indicate two lines of thematic clusters (see Fig.  3 -A network graph depicting the citing and being cited patterns in the research corpus. Node sizes were defined by their citation count and betweenness centrality.). These clusters align with the results of the analysis of the titles, abstracts, and keywords of the sampled publications and forge the earlier themes (Theme 4: Emergency remote education and Theme 5: Pedagogy of care).

• Thematic Cluster 1: The first cluster centers on the abilities of educational response, emergency remote education affordances, and continuity of education (Bozkurt & Sharma, 2020a ; Crawford et al., 2020 ; Hodges et al., 2020 ) to mitigate the impact of COVID-19 on education, especially for more vulnerable and disadvantaged groups (UNESCO, 2020 ; Viner et al., 2020 ). The thematic cluster one agrees with the theme four emergency remote education . The first trend line (See red line in Fig.  3 ) shows that the education system is vulnerable to external threats. Considering that interruption of education is not exclusive to pandemics – for example, political crises have also caused disruptions (Rapp et al., 2016 ) – it is clear that coping mechanisms are needed to ensure the continuity of education under all conditions. In this case, we need to reimagine and recalibrate education to make it resilient, flexible, and adaptive, not only to ensure the continuity of education, but also to ensure social justice, equity, and equality. Given that online education has its own limitations (e.g., it is restricted to online tools and infrastructures), we need to identify alternative entry points for those who do not have digital devices or lack access to the internet.

“What we teach in these times can have secondary importance. We have to keep in mind that students will remember not the educational content delivered, but how they felt during these hard times. With an empathetic approach, the story will not center on how to successfully deliver educational content, but it will be on how learners narrate these times” (p. iv).

Conclusion and Suggestions

The results from this study indicate that quick adaptability and flexibility have been key to surviving the substantial challenges generated by COVID-19. However, extreme demands on flexibility have taken a toll on human well-being and have exacerbated systemic issues like inequity and inequality. Using data mining that involved network analysis and text mining as analytical tools, this research provides a panoramic picture of the COVID-19-related themes educational researchers have addressed in their work. A sample of 1150 references yielded seven themes, which served to provide a comprehensive meta-narrative about COVID-19 and its impact on education.

A portion of the sampled publications focused on what we refer to as the great reset , highlighting the challenges that the emergency lockdown brought to the world. A publication pattern centered around digital pedagogy posited distance and online learning as key components and identified the need for teacher training. Given the need for adaptability, a third theme revealed the demand for professional development in higher education and a future shift in educational roles. It can be recommended that future research investigate institutional policy changes and the adaptation to these changes in renewed educational roles. The ERE theme centered on the lack of preparation in instituting the forced changes brought about by the COVID-19 pandemic. The publications related to this theme revealed that the COVID-19 pandemic uncovered silent threads in educational environments, like depression, inequality, and injustice. A pedagogy of care has been developed with the aim of reducing anxiety and providing support through coping strategies. These research patterns indicate that the future of education demands sustainability and resilience in the face of uncertainty.

Results of the thematic analysis of citation patterns (Fig.  3 ) overlapped with two of the themes found in our thematic concept map (Fig.  1 ) and network graphic (Fig.  2 ). It was shown that researchers have emphasized the continuity of education and the psychological effects of the COVID-19 crisis on learners. Creating coping strategies to deal with global crises (e.g., pandemics, political upheavals, natural disasters) has been shown to be a priority for educational researchers. The pedagogy of resilience (Purdue University Innovative learning, n.d. ) provides governments, institutions, and instructors with an alternative tool to applying to their contexts in the face of hardship. Furthermore, prioritizing the psychological long-term effects of the crisis in learners could alleviate achievement gaps. We recommend that researchers support grieving learners through care (Noddings, 1984 ) and trauma-informed pedagogy (Imad, 2020 ). Our resilience and empathy will reflect our preparedness for impending crises. The thematic analysis of citation patterns (1: educational response, emergency remote education affordances, and continuity of education; 2: psychological impact of COVID-19) further indicates suggestions for future instructional/learning designers. Freire ( 1985 ) argues that to transform the world we need to humanize it. Supporting that argument, the need for human-centered pedagogical approaches (Robinson et al., 2020 ) by considering learning a multifaceted process (Hodges et al., 2021 ) for well-designed learning experiences (Moore et al., 2021 ) is a requirement and instructional/learning designers have an important responsibility not only to design courses but an entire learning ecosystem where diversity, sensitivity, and inclusivity are prioritized.

ERE is not a representative feature in the field of online education or distance education but rather, a forced reaction to extraordinary circumstances in education. The increasing confusion between the practice of ERE and online learning could have catastrophic consequences in learners' outcomes, teachers' instructional practices, and institutional policies. Researchers, educators, and policymakers must work cooperatively and be guided by sound work in the field of distance learning to design nourishing educational environments that serve students’ best interests.

In this study, text mining and social network analysis were demonstrated to be powerful tools for exploring and visualizing patterns in COVID-19-related educational research. However, a more in-depth examination is still needed to synthesize effective strategies that can be used to support us in future crises. Systematic reviews that use classical manual coding techniques may take more time but increase our understanding of a phenomenon and help us to develop specific action plans. Future systematic reviews can use the seven themes identified in this study to analyze primary studies and find strategies that counteract the survival of the fittest mindset to ensure that no student is left behind.

Acknowledgements

This paper is dedicated to all educators and instructional/learning designers who ensured the continuity of education during the tough times of the COVID-19 pandemic.

This article is produced as a part of the 2020 AECT Mentoring Program.

SNA of references covering pre-COVID-19 period (Only the first authors were labeled)

Authors’ Contributions

AB: Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Data Curation, Writing—Original Draft, Writing—Review & Editing, Visualization, Funding acquisition.; KK: Conceptualization, Investigation, Writing—Original Draft, Writing—Review & Editing.; MT: Conceptualization, Investigation, Writing—Original Draft, Writing—Review & Editing.; ÖK: Conceptualization, Investigation, Writing—Original Draft, Writing—Review & Editing.; DCR: Conceptualization, Investigation, Writing—Original Draft, Writing—Review & Editing.

This paper is supported by Anadolu University, Scientific Research Commission with grant no: 2106E084.

Data Availability

Declarations.

This is a systematic review study and exempt from ethical approval.

The authors declare no competing interests.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Original research article, impacts of the covid-19 pandemic on student learning and opportunity gaps across the 2020–2021 school year: a national survey of teachers.

• 1 School of Education, University of Delaware, Newark, DE, United States
• 2 Department of Teaching, Learning, and Culture, Texas A&M University, College Station, TX, United States
• 3 School of Education, University of California, Irvine, Irvine, CA, United States
• 4 Department of Special Education and Communication Disorders, University of Nebraska-Lincoln, Lincoln, NE, United States

Although many school districts made efforts to provide instruction during the COVID-19 pandemic (including in-person, remote, and blended/hybrid options the length of instruction time and delivery models have varied from district to district. This disruption in education has been projected to result in a significant learning loss, which may be particularly profound for students from disadvantaged backgrounds, leading to widening opportunity gaps. However, there is limited empirical data that can provide important contextual background for understanding the impact of the pandemic on student learning. Therefore, we conducted a national survey with a random sample of 582 elementary school teachers to understand the instructional changes that occurred, the amount of academic content instruction provided to students, and teachers perceptions of the learning supports needed and provided to students during the 2020–2021 school year. Results indicated that most teachers relied on alternative forms of instruction and experienced changes in delivery models but reported low instructional effectiveness. Compared to typical years, teachers reported significant decreases in curriculum coverage; the number of students who received needed interventions, and students who were ready to transition to the next grade level during the 2020–2021 school year. Teachers also reported greater impacts on instruction for students from disadvantaged backgrounds. Follow-up analyses using prior school achievement data corroborated the findings that higher school achievement was associated with smaller impacts on student learning and delivery of instruction.

Introduction

Many school districts were forced to temporarily close schools in spring 2020 during the COVID-19 crisis. This marked one of the largest disruptions to education in history, forcing more than 1.6 billion children out of school in the United States and affecting 95% of school-aged children worldwide ( United Nations, 2020 ; Kaffenberger, 2021 ). Although many schools attempted to provide remote instruction during the spring of 2020, estimates suggest that between 7.2 and 11.6 million K-5 students also may not have received remote instruction ( Goodrich et al., 2022 ).

In the 2020–2021 school year, school districts adapted to the pandemic by developing a variety of instructional models to reach students, including remote learning, blended/hybrid learning, and in-person learning, with some school districts implementing multiple models for various lengths of time. Despite the strong efforts of schools and teachers, these delivery models may not have provided the same quality of education compared to instruction prior to the pandemic for several reasons. Schools that returned to in-person learning faced frequent student absences and staff shortages due to the COVID related quarantines. For schools that opted to provide alternate forms of learning, many teachers, parents, and students had to make quick transitions without strong supports in place (e.g., technology support, student engagement strategies; Stanistreet et al., 2020 ).

To estimate the potential impact of the COVID-19 school closures on student learning (e.g., Dorn et al., 2020 ; Kuhfeld et al., 2020 ; Kaffenberger, 2021 ), some researchers have projected learning losses based on estimates from typical school closures, such as summer breaks. With some variability in the estimates of the summer learning loss (0.001 to 0.01 SD learning loss per day out of school), prior research indicated student achievement slows down or even declines over the summer breaks (e.g., von Hippel et al., 2018 ; Kuhfeld, 2019 ). Based on these estimates, Kuhfeld et al. (2020) projected that students who did not have access to remote instruction (3 months) in spring 2020 would begin fall 2020 with only 37% to 68% of typical learning gains in reading and mathematics, and some students may be up to one year behind in mathematics. Even students who received remote instruction in spring 2020 were projected to begin fall 2020 with 60% to 87% of their typical learning gains.

However, these numbers may underestimate the problem to some degree. The assumption is that learning losses could be similar to losses experienced during other breaks from school. However, instructional challenges related to COVID-19 are also likely to have resulted in less content coverage when school has been in session, compounding the losses. In addition, differential access to technology and remote instruction during COVID-19 school closures are projected to exacerbate the impacts of the pandemic for some populations, widening SES-based opportunity gaps. The learning losses are expected to be greatest among low-income students because students from high SES schools were estimated to receive more remote instruction than students from low SES schools ( Kuhfeld et al., 2020 ). Even when students from low SES schools were able to access remote instruction, they were less likely to have the same high-quality remote learning or supportive environments (e.g., parental academic supervision, space with minimal distraction; Dorn et al., 2020 ). Dorn et al. (2020) projected that low-income students would experience 12.4 months of learning loss compared to the overall average learning loss of 6.8 months, exacerbating the existing opportunity gaps by 15% to 20%.

Some researchers have suggested that the short-term learning losses due to the pandemic may be cumulative and result in larger and permanent learning losses (e.g., Dorn et al., 2020 ; Kaffenberger, 2021 ). Dorn et al. (2020) estimated that the pandemic is likely to lead to higher high-school dropout rates (i.e., 2–9% increase to the current 5% rate) due to decreased academic engagement and achievement, and disruptions to supports that can help students stay in school (e.g., community support, youth-serving organizations), leading to long-term economic issues. Kaffenberger (2021) reported that learning loss in grade 3 would accumulate and result in students performing 1 to 1.5 years lower in grade 10. He also estimated that short-term remediation efforts (e.g., teachers covering 1/2 of grade 3 curriculum in grade 4 and reverting to the pre-pandemic curriculum and instructional levels by grade 5) would reduce the long-term learning loss to one-half of a school year. The long-term remediation efforts (e.g., identifying students’ learning levels via formative assessments, adapting teacher instructions) were estimated to fully mitigate the learning loss. That said, the pandemic is still on-going (with some school closures occurring again in early 2022 due to COVID-19 variants), and it is unlikely that schools and teachers were able to cover the same amount of content in the 2020–2021 school year as in typical years, or that they were able to provide the same levels of support to students from minoritized and disadvantaged populations that they do in typical years.

Despite these projections, the empirical data to evaluate the actual impact of the COVID-19 pandemic on student learning is limited. Engzell et al. (2021) used national assessments conducted before (January to February) and after (June) the COVID-19 lockdown in the Netherlands. They compared student progress in mathematics, reading, and spelling on the national assessments during 2020 to student progress in the three previous years. Results indicated a learning loss equivalent to 3 percentile points despite the relatively short lockdown. However, the learning loss was up to 60% greater among students from disadvantaged backgrounds (i.e., students from less-educated households), indicating the disproportionate impact of the pandemic on student learning. Similarly, achievement scores on state assessments for students in grades 4 to 8 in 17 school districts in Illinois indicated that students scored significantly lower than expected in mathematics compared to prior to the pandemic, resulting in a learning losses as large as 56% of a school year ( Streich et al., 2021 ). Furthermore, special education status, English language learner status, and eligibility for free/reduced price lunch were associated with greater learning losses in mathematics among middle school students.

Taken together, although prior research has shown varying levels of impact of the COVID-19 pandemic on student learning, it is evident that student learning was disrupted, leading to short-term and long-term detrimental effects on student achievement and educational attainment. Prior research also suggests that this learning loss may be particularly profound for students from disadvantaged backgrounds, leading to widening opportunity gaps. However, the current literature and our understanding of the impact of the pandemic on student learning is primarily based on model-based projections and limited empirical data comparing student performance prior to and after the lockdown. Detailed empirical data that can provide important contextual background for understanding the impact of the pandemic on student learning are missing.

Therefore, the purpose of the present study was to understand the instructional changes that occurred during the 2020–2021 school year and their impact on student learning from a national sample of elementary school teachers. Additionally, we sought information from teachers regarding the amount of academic content instruction provided to students and teachers’ perceptions of the learning supports needed and provided to students across the 2020–2021 school year. Furthermore, we aimed to explore whether teacher reported changes were related to prior school achievement data.

Materials and Methods

Participants.

For survey distribution, we obtained a representative random sample of K-5 educator email addresses that was proportionally reflective of the number of teachers in each grade (K-5) as well as representative of the distribution of the United States population across different geographic regions survey distribution from Market Data Retrieval (MDR). We made sure that only one teacher from each school was selected to maximize the number of schools. We calculated the total number of respondents ( N = 382) needed to achieve a margin of error of ± 5.0% with a 95% confidence interval ( Dillman, 2000 ). After excluding 289 invalid email address, we sent 9,476 teachers the invitation to complete the survey. Of those, 595 teachers provided consent, and 13 teachers who did not answer any questions were later excluded. The final sample consisted of 582 teachers, providing this survey with a ± 4.1% margin of error with a 95% confidence interval.

Stanford Education Data Archive (SEDA; Reardon et al., 2021 ) provides demographics (e.g., region, gender, socioeconomic status, race) and academic achievement data (e.g., mathematics, English language arts) for all tested students in grades 3–8 in public schools across the United States averaged over the 2008–2009 to 2017–2018 school years. SEDA school-level mean mathematics and English language arts achievement data were available for 490 teachers, and covariate data were available for 515 teachers who responded to our survey. Given the lower than anticipated response rate (6.14%), we compared teachers who did and did not respond to the survey to ensure the generalizability of our findings. After correcting for Type I error rate, there were some statistically significant differences by geographic region. The Mid-Atlantic and South-Central regions were significantly under-represented in survey responders, whereas the Mountain and North-Central regions were significantly over-represented among survey responders. There were no other significant differences. We also compared our sample of teachers to national teacher demographics reported by the National Center for Educational Statistics ( Hussar et al., 2020 ). Overall, our sample approximated the national averages in terms of gender and race. However, teachers with over 20 years of teaching experience were over-represented in our samples (32.3%) compared to the national average (22.4%).

Survey Questions

We created and administered the survey using the Qualtrics electronic survey platform. The survey consisted of 59 items. The first eight questions were on demographics of teachers and students in their classrooms. Next, teachers answered questions about the instructional model(s) used by their schools. Additionally, we asked questions related to student progress and instruction in three specific academic content areas: reading, mathematics, and writing. The questions included the amount of planned curriculum teachers were able to cover, percentage of students needing extra support in each academic area, percentage of students who did not receive needed support for each academic area during the 2020–2021 school year compared to typical years prior to the COVID pandemic, and whether these changes were due to the pandemic. Teachers also rated the negative impacts the pandemic had on students overall, as well as on subpopulations of students (i.e., students from low-income backgrounds, students with IEPs, students who are English language learners). They also rated their perceived effectiveness of remote instruction. Finally, teachers answered questions about their opinions regarding the effectiveness of instruction during the pandemic.

Overall, our respondents had a mean of 15.44 years of teaching experience ( SD = 9.65) and a mean of 23 students in their class ( SD = 9.51) at the time of the survey. The majority (80.2%) reported having less than 20% of students with IEPs in their classroom. Similarly, 79.5% of teachers reported having classrooms with less than 20% English language learners.

Descriptive Analysis

Instructional model.

Figure 1 shows the instructional models teachers reported for their schools at the start and end of the 2020–2021 school year. At the start of the school year, most schools offered either 100% remote instruction (46.7%) or in-person instruction with an option for remote instruction (30.9%). Approximately 12.1% of schools offered hybrid, and only 8.6% of schools offered 100% in-person instruction. However, approximately 60% of teachers experienced a change in their instructional model from the beginning to the end of the school year. At the end of the school year, most schools offered in-person instruction with an option for remote instruction (65.2%), followed by 100% in-person (16.5%), hybrid (13.0%), and 100% remote (2.6%) instruction. Thus, the number of schools offering 100% in-person or in-person instruction with an option for remote instruction doubled from the beginning to the end of the 2020–2021 school year.

Figure 1. Teacher-reported school instructional models.

More specifically, 64.7% of teachers indicated that their instruction was 100% in person at least part of the 2020–2021 school year whereas 35.3% of teachers indicated that they never offered 100% in-person instruction. Among teachers who reported using a 100% in-person instructional model for at least part of the year, the percentage of the school year for which their school provided 100% in-person instruction varied: less than 20% of the school year (16.3%), between 21 and 40% (19.3%), between 41 and 60% of the year (15.2%), between 80 and 99% (15.8%), and 100% (19.0%).

Student Progress and Instruction in Academic Content Areas

Curriculum coverage.

Overall, teachers reported a significant decrease in the amount of planned curriculum they were able to cover in academic content areas (i.e., reading, mathematics, and writing) during the 2020–2021 school year compared to typical years. Figure 2 shows the percentage of planned curriculum teachers were able to cover in each academic area. During typical years prior to the COVID-19 pandemic, 93.3% of teachers indicated that they were able to cover more than 80% of planned curriculum in reading compared to only 43.8% of teachers during the 2020–2021 school year. In other words, more than half the teachers who responded to the survey (56.3%) were not able to cover 80% of their planned reading curriculum during the 2020–2021 school year, compared to only 6.7% of teachers during typical years. This pattern of findings was similar for mathematics. Only 53.2% of teachers reported that they were able to cover more than 80% of their planned curriculum in mathematics compared to 92.8% of teachers in typical years. For writing, about 30.9% of teachers indicated that they were able to cover more than 80% of planned curriculum during the 2020–2021 school year compared to 79.5% of teachers during typical years. Most teachers (85.4%) indicated that this change in their ability to cover the curriculum during the 2020–2021 school year was due to the pandemic. Other reasons reported by 4.8% of teachers included student absences, having a new administration team, and other natural disasters in addition to the pandemic.

Figure 2. Percentage of curriculum covered in each academic area.

Students Needing Extra Support/Intervention

Teachers indicated that fewer students who needed extra support and/or intervention in academic content areas actually received the support during the 2020–2021 school year compared to typical years. During typical years, teachers reported students were able to receive extra support/intervention they needed in reading (74.9%), mathematics (71.2%), and writing (70.2%). However, there was a significant decrease in the percentage of teachers who indicated that students received the needed support during the 2020–2021 school year: 44.3% in reading, 49.2% in mathematics, and 41.9% in writing.

Student Readiness for Transition

Teachers reported fewer students were ready to transition to the next grade level at the end of 2020–2021 school year compared to typical years (see Figure 3 ). Whereas 68.9% of teachers indicated at least 80% of their students being ready to transition to the next grade in typical years, only about 31.9% of teachers reported at least 80% of their students were ready to transition to the next grade at the end of the 2020–2021 school year. About 29.4% of teachers indicated that less than 60% of their students were ready to transition to the next grade level compared to only 4.5% of teachers indicating less than 60% of their students ready to transition in typical years. The majority of teachers (65.5%) indicated that this drop in the percentage of students ready to transition to the next grade was due to the COVID-19 pandemic. A small portion of respondents (6.2%) indicated other reasons, which included a lack of student participation, lack of teacher knowledge, and lack of high-quality instruction.

Figure 3. Percentage of students ready to transition to next grade.

Subpopulations of Students

Teachers rated the impact of the pandemic on their delivery of academic skills instruction on a 0 (no impact, delivery of academic instruction was typical) to 10 (high impact, students missed significant instructional time, delivery of instruction was very challenging, many students are behind) scale. Overall, the mean rating was 6.67 ( SD = 2.64), indicating a moderate to large impact of the pandemic on teachers’ delivery of academic instruction. Teachers indicated significantly greater impacts for students from low-income backgrounds ( M = 7.74, SD = 2.59) compared to those who were not from low-income backgrounds ( M = 4.83, SD = 2.59), t (457) = 24.04, p < 0.001. Teachers also rated significantly greater impacts for students with IEPs ( M = 7.43, SD = 2.90) compared to those without IEPs ( M = 5.51, SD = 2.71), t (455) = 15.64, p < 0.001. Finally, teachers rated significantly greater impacts for English language learners ( M = 7.31, SD = 2.88) compared to non-English language learners ( M = 5.45, SD = 2.84), t (389) = 13.78, p < 0.001.

Overall, teachers rated that remote instruction was significantly less effective for students from disadvantaged backgrounds. Teachers rated remote instruction being more effective for students who were not from low-income backgrounds ( M = 5.66, SD = 2.51) compared to students from low-income backgrounds ( M = 4.13, SD = 3.02), t (432) = −10.17, p < 0.001. Teachers also rated that remote instruction was more effective for students without IEPs ( M = 5.45, SD = 2.43) than it was for those with IEPs ( M = 3.84, SD = 3.05), t (421) = −11.22, p < 0.001. Lastly, teachers rated that remote instruction was significantly more effective for students who were not English language learners ( M = 5.45, SD = 2.53) compared to English language learners ( M = 3.92, SD = 3.05), t (356) = −9.13, p < 0.001.

Inferential Analysis

Zero-order correlations.

Our third research question focused on the relations between school achievement indexed by SEDA and various survey questions, including use of a 100% in-person instructional model, percentage of students ready to transition to the next grade level in Spring 2021, overall impact of the pandemic on academic skills instruction, and the impact of the pandemic on teachers’ ability to cover the curriculum and provide intervention for specific academic skills. There was a small correlation between school achievement and the percentage of time in which a 100% in-person instructional model was used ( r = 0.19, p < 0.001), indicating higher achieving schools provided 100% in-person instruction more often than low achieving schools.

School achievement was moderately negatively correlated with overall ratings of the impact of the pandemic ( r = −0.29, p < 0.001) and with teacher-reported impacts of the pandemic on the percentage of students ready to transition to the next grade level ( r = −0.30, p < 0.001). This pattern of results indicated that teachers at higher achieving schools reported fewer negative effects of the pandemic, and teachers at higher achieving schools reported smaller differences in the number of students ready to transition to the next grade level between the 2020 and 2021 school year and typical years prior to the pandemic. School achievement was also correlated with teacher-reported impacts of the pandemic on specific academic content areas, but these correlations were small ( r s range from −0.11 to −0.19, all p s < 0.05). There were small correlations between the percent of the year a 100% in-person instructional model was used and teacher-reported impacts of the pandemic ( r s range from −0.18 to −0.22, all p s < 0.001), indicating that teachers who used more in-person instruction reported smaller impacts of the pandemic on their ability to cover the curriculum and the percentage of students who needed supplemental intervention for academic skills instruction.

Regression Analysis

To further evaluate our third research question, we examined predictors of the overall impact of the pandemic and the amount of the curriculum that was covered in reading, writing, and mathematics in the 2020–2021 school year, including SEDA school mean achievement and percentage of time in which a 100% in-person instructional model was used. Regression models predicting amount of curriculum covered in the 2020–2021 school year controlled for teacher reports of the amount of curriculum covered in typical years. Results are presented in Table 1 . We note that negative correlations for overall impact indicate that more in-person instruction and higher achieving schools experienced fewer negative effects of the pandemic. Positive correlations for coverage of reading, writing, and mathematics curriculum indicate that more in-person instruction and higher achieving schools were associated with covering more of the planned curriculum for academic skills. Both school achievement and percentage of time using a 100% in-person instructional model were significantly predictive of overall impacts of the pandemic and teacher reported coverage of the reading, writing, and mathematics curriculum, even after controlling for teacher reported coverage of the curriculum in typical years. Higher school achievement and more use of a 100% in-person instructional model were associated with smaller negative impacts of the pandemic and greater coverage of academic curricula.

Table 1. Standardized regression coefficients predicting overall impact and coverage of curriculum.

Finally, we used logistic regression analysis to examine whether SEDA school achievement and percentage of time using a 100% in-person instructional model predicted whether there were students who needed extra intervention in reading, writing, and mathematics but did not receive it in the 2020–2021 school year, after controlling for whether there were students who needed extra intervention but did not receive it in typical years. These results are presented in Table 2 . Use of a 100% in-person instructional model was only significant for mathematics, indicating that teachers who reported using more 100% in-person instruction were less likely to report having students who needed extra mathematics intervention but did not receive it; however, the magnitude of this effect was small. In contrast, higher achieving schools were significantly less likely than lower achieving schools to have students who needed additional intervention but did not receive it, even after controlling for students needing but not receiving intervention in typical years.

Table 2. Logistic regression models predicting whether students who needed additional supports for academic skills did not receive them.

Successes and Challenges of Instruction

For questions related to the successes and challenges of remote and in-person instruction during the 2020–2021 school year, teachers were allowed to indicate multiple items (i.e., check all that apply). Teachers indicated that having a lower teacher-student ratio would contribute to successful remote instruction (61.7%) followed by the structures and scheduling of remote instruction (52.6%), training opportunities (45%), and support personnel (e.g., paraprofessionals, 38.5%). The majority of other responses included having parental support at home and students’ access to better technology (internet access, remote instruction platform support), and having a teacher dedicated to remote instruction.

Teachers also indicated that distractions in students’ homes (71.1%), internet access/availability (61.0%), student attendance (60.8%), lack of face-to-face interactions with students (57.7%), difficulty with evaluating student work (55.7%), difficulty with monitoring student progress (48.8%), managing remote and in-person instruction simultaneously (42.6%), and difficulty with providing feedback on student work (40.7%) as challenges associated with delivering remote instruction. Other challenges included a lack of parental support/involvement, lack of student engagement, and parents or other household members completing student assignments or assessments.

Despite these challenges, teachers indicated that some positive takeaways during the 2020–2021 school year were students being more conscientious (68.6%), greater ability to provide individualized attention due to reduced class sizes or alternating days (21.6%), and more time for students to participate in academics due to reductions in extracurricular activities (21.1%). About 24.6% of teachers indicated that there were no positive takeaways from the 2020–2021 school year.

The results of this survey provide important context about the instructional models used by schools during the 2020–2021 school year, how content coverage may compound issues related to learning losses in academic areas, and factors that may be related to the ability of schools to cover content and support students. Several studies have demonstrated that student achievement has been lower during the pandemic compared to prior to the pandemic, with estimates ranging from three percentile points in the Netherlands ( Engzell et al., 2021 ) to more than half of a school year in the U.S. state of Illinois ( Streich et al., 2021 ). Moreover, students’ academic motivation and participation in extracurricular activities, as perceived by their parents, decreased significantly during the COVID-19 pandemic ( Zaccoletti et al., 2020 ).

Yet, the pandemic is not over, and with the continued struggle with the COVID variants in 2022 currently, students may be falling even further behind. The results of this survey suggest that most teachers were not able to cover at least 80% of their reading, writing, and mathematics curriculum, which was significantly lower than their reported ability to cover 80% of the curriculum in previous years. Teachers also clearly indicated that many fewer students were ready to transition to the next grade level at the end of the 2020–2021 school year. Using average reported class sizes and teacher responses for students not ready to transition to the next grade level, we estimated that 32.4% of students were not ready to transition, as compared to 13.9% in previous years (an increase of 18.5%). With 21.2 million K-5 students attending school in 2020 ( National Center for Education Statistics, 2021 ), this means nearly 3.9 million more students (6.8 million total) were not ready to transition to the next grade, with likely disproportionate impacts on minoritized students.

Our findings also indicated that many students who needed extra support/intervention in the academic content areas did not receive needed support in the 2020–2021 school year. This is alarming because it has likely compounded learning losses already realized during school shutdowns in spring of 2020, and some schools still may not be fully covering the academic curriculum in the 2021–2022 school year. This suggests there will be long term and compounded effects if teachers continue to have difficulty implementing the full curriculum. Therefore, our findings call for immediate recovery efforts.

Kaffenberger (2021) projected that short-term (e.g., covering previous year’s curriculum before revering to the pre-pandemic curriculum) and long-term efforts (e.g., identifying students’ needs using formative assessments, adapting teacher instruction to students’ levels and needs) can reduce/remediate the learning loss. Therefore, substantial restructuring of current pre-pandemic curricula may be inevitable to minimize the compounded effects. In addition, some states have initiated alternative ways to offer additional instruction (e.g., Tennessee Tutoring Coprs). Continued efforts should be made to find alternative and innovative ways to provide additional learning opportunities to remediate the learning loss. Beyond the immediate educational needs, Fusco et al. (2021) suggested providing career support for students to better prepare them for the economic crisis and changes following the COVID-19 pandemic.

Our survey results indicated that most teachers relied on alternative forms of instruction and experienced changes during the 2020–2021 school year. Yet, the overall rating for teacher-reported effectiveness of remote instruction was low ( M = 4.74). This finding suggests that continued development of high-quality online educational learning and support is also needed. Moreover, Zhu and Liu (2020) called for more quantitative and qualitative research to evaluate remote teaching and learning, and long-term sustainability. Consistent with teachers’ reports in our survey, as well as in Goodrich et al. (2022) , more systematic training for school personnel is needed to improve the quality of remote instruction. Additionally, prior studies have found that family, school, and peer support increases student engagement, which in turn improves academic competence and achievement (e.g., Elias and Haynes, 2008 ; Estell and Perdue, 2013 ). As much as in-person school engagement is important to academic achievement and school completion, student engagement during remote instruction may be critical to promoting successful remote learning. Teachers who responded to our survey did note a lack of student engagement and parental support/involvement as a challenge to providing remote instruction. Such support from family, school, and peers may be especially important for students from disadvantaged backgrounds ( Elias and Haynes, 2008 ).

Our survey results also add to the growing literature that the impacts of the COVID-19 pandemic on academic learning have disproportionately affected low-income students, minoritized students, and students with disabilities (see Dorn et al., 2020 ; Goodrich et al., 2022 ). In the current survey, teachers reported greater impacts of the pandemic on academic instruction for students with IEPs, low-income students, and English language learners. Our regression analyses corroborate these findings across schools as well, as higher school achievement was associated with smaller negative impacts on the curriculum coverage and fewer students requiring additional intervention. Our results also indicate that teachers in higher performing schools did not have to alter their instruction as much as teachers in lower performing schools. This may have played a role in the reported curriculum coverage and associated learning losses, as our results indicated that the amount of in-person instruction significantly contributed to teachers’ ability to cover the curriculum. These findings are important to consider when allocating resources for pandemic recovery efforts. Moreover, the COVID-19 induced economic damage and educational budget cuts are likely to have a greater impact on students from disadvantaged backgrounds. Recovery efforts should be considered carefully, so that they do not reinforce existing inequalities.

Our findings also add to the literature in an important way by providing teachers with an opportunity to identify other factors that may have contributed to their ability to cover the curricula and support their students. This can offer Federal and State Departments of Education with areas of opportunity for providing teachers with support, funding, or intervention resources. For example, teachers consistently reported that personnel and training resources can contribute to better implementation of instruction (including remote instruction). Solutions might include increasing the number of paraprofessionals to assist with instruction and/or providing training opportunities to teachers and paraprofessionals.

Positive Take-Aways and Potential Solutions

Approximately 75% of teachers indicated that there were also some positives that came out of the pandemic, including increases in student conscientiousness, prioritization of some academic content, and systems that resulted in more individual attention. Policymakers and administrators may want to consider thinking more flexibly about school schedules and supports for teachers and students moving forward. Alternating days for instruction for students to reduce class sizes may not be desirable or feasible in the long-term, but there may be other creative approaches to continue capitalize on the benefits of smaller student groupings, such as staggering start and end times for the school day.

Limitations

The samples of teachers who completed our surveys were generally representative of the population of teachers in the United States However, a large percentage of teachers did not respond to the surveys. Although responders and non-responders were similar in key demographic variables (e.g., SES, school setting, school type, grade level taught), it is possible that low response rate resulted in selection bias. It is also possible that the teachers may have under- or over-estimated other descriptive variables for their classrooms or were unaware of some of the school services provided by resource and special education teachers.

Implications and Conclusion

Schools in the United States have a large problem on their hands. Along with learning losses, many teachers report not covering as much of the academic curricula for students, especially in schools with lower achievement levels. This is an ongoing problem that is likely to be exacerbated, and it will likely continue to widen the opportunity gaps for minoritized students, low-income students, and students with disabilities. Policymakers, school administrators, and teachers must be cognizant of the challenges with implementing instruction consistently to adequately cover the necessary content each year, and even increase the content coverage and student support to accelerate recovery efforts. Of course, these considerations need to be weighed against public health safety, which is an important factor in deciding which educational models to implement. It will also be important for educational decision makers to consider these teacher report findings when allocating recovery resources, such as prioritizing lower achieving schools and students from disadvantaged backgrounds.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

The studies involving human participants were reviewed and approved by IRB at the University of Nebraska-Lincoln. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

JN, JG, MH, and NK were equally responsible for the conduct of this research. All authors helped formulate the research questions to be included in surveys, assisted with survey distribution, data cleaning and analysis, and writing survey results for publication.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Zhu, X., and Liu, J. (2020). Education in and after COVID0-19: immediate responses and long-term visions. Postdigit. Sci. Educ. 2, 695–699. doi: 10.1007/s42438-020-00126-3

Keywords : COVID-19, survey research, elementary school, academic instruction, opportunity gaps

Citation: Namkung JM, Goodrich JM, Hebert M and Koziol N (2022) Impacts of the COVID-19 Pandemic on Student Learning and Opportunity Gaps Across the 2020–2021 School Year: A National Survey of Teachers. Front. Educ. 7:921497. doi: 10.3389/feduc.2022.921497

Received: 15 April 2022; Accepted: 10 June 2022; Published: 07 July 2022.

Reviewed by:

Copyright © 2022 Namkung, Goodrich, Hebert and Koziol. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Jessica M. Namkung, [email protected]

The Impact of COVID-19 on Student Experiences and Expectations: Evidence from a Survey

In order to understand the impact of the COVID-19 pandemic on higher education, we surveyed approximately 1,500 students at one of the largest public institutions in the United States using an instrument designed to recover the causal impact of the pandemic on students' current and expected outcomes. Results show large negative effects across many dimensions. Due to COVID-19: 13% of students have delayed graduation, 40% lost a job, internship, or a job offer, and 29% expect to earn less at age 35. Moreover, these effects have been highly heterogeneous. One quarter of students increased their study time by more than 4 hours per week due to COVID-19, while another quarter decreased their study time by more than 5 hours per week. This heterogeneity often followed existing socioeconomic divides; lower-income students are 55% more likely to have delayed graduation due to COVID-19 than their higher-income peers. Finally, we show that the economic and health related shocks induced by COVID-19 vary systematically by socioeconomic factors and constitute key mediators in explaining the large (and heterogeneous) effects of the pandemic.

Noah Deitrick and Adam Streff provided excellent research assistance. All errors that remain are ours. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

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Research Article

Effects of the COVID-19 pandemic in higher education: A data driven analysis for the knowledge acquisition process

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations Instituto de Física de Líquidos y Sistemas Biológicos (UNLP-CONICET), La Plata, Argentina, Departamento de Ciencias Básicas, Facultad de Ingeniería, Universidad Nacional de La Plata (UNLP), La Plata, Argentina, Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, Palma de Mallorca, Spain

Roles Data curation, Formal analysis, Methodology, Software, Writing – original draft, Writing – review & editing

Affiliation Departamento de Física Médica, Centro Atómico Bariloche, CONICET, CNEA, Bariloche, Argentina

Roles Data curation, Formal analysis, Methodology, Software, Validation, Writing – original draft, Writing – review & editing

Affiliation Departamento de Estadística, Centro Regional Universitario Bariloche (CRUB) Universidad Nacional del Comahue (UNCOMA), Neuquén, Argentina

Roles Conceptualization, Formal analysis, Investigation, Methodology, Supervision, Writing – original draft, Writing – review & editing

Affiliations División Física Estadística e Interdisciplinaria, Centro Atómico Bariloche and CONICET, Bariloche, Argentina, Profesorado en Física, Universidad Nacional de Río Negro (UNRN), Bariloche, Argentina

• Fátima Velásquez-Rojas, 
• Jesus E. Fajardo, 
• Daniela Zacharías, 
• María Fabiana Laguna

• Published: September 7, 2022
• https://doi.org/10.1371/journal.pone.0274039
• Reader Comments

The COVID-19 pandemic abruptly changed the classroom context and presented enormous challenges for all actors in the educational process, who had to overcome multiple difficulties and incorporate new strategies and tools to construct new knowledge. In this work we analyze how student performance was affected, for a particular case of higher education in La Plata, Argentina. We developed an analytical model for the knowledge acquisition process, based on a series of surveys and information on academic performance in both contexts: face-to-face (before the onset of the pandemic) and virtual (during confinement) with 173 students during 2019 and 2020. The information collected allowed us to construct an adequate representation of the process that takes into account the main contributions common to all individuals. We analyzed the significance of the model by means of Artificial Neural Networks and a Multiple Linear Regression Method. We found that the virtual context produced a decrease in motivation to learn. Moreover, the emerging network of contacts built from the interaction between peers reveals different structures in both contexts. In all cases, interaction with teachers turned out to be of the utmost importance in the process of acquiring knowledge. Our results indicate that this process was also strongly influenced by the availability of resources of each student. This reflects the reality of a developing country, which experienced prolonged isolation, giving way to a particular learning context in which we were able to identify key factors that could guide the design of strategies in similar scenarios.

Citation: Velásquez-Rojas F, Fajardo JE, Zacharías D, Laguna MF (2022) Effects of the COVID-19 pandemic in higher education: A data driven analysis for the knowledge acquisition process. PLoS ONE 17(9): e0274039. https://doi.org/10.1371/journal.pone.0274039

Editor: Jianguo Wang, China University of Mining and Technology, CHINA

Received: September 7, 2021; Accepted: August 19, 2022; Published: September 7, 2022

Copyright: © 2022 Velásquez-Rojas et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting information files.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The process of acquiring knowledge is one of the most complex for the human being since it involves individual and social processes that have been studied by various epistemological currents [ 1 ]. The educational context where this process is developed is of great relevance since it represents the meeting space between teachers and students, in which a fundamental part of the construction of new knowledge occurs.

The COVID-19 pandemic abruptly changed this context with classroom closures of unprecedented extent and duration, disrupting conventional education in schools and universities around the world. Such measures were an extension of the isolation established in many countries to mitigate the effects of COVID-19, given that social distancing proved to be one of the most effective strategies [ 2 – 10 ].

The educational community as a whole made an enormous effort to quickly adapt to the distance and online learning that this lockdown brought [ 11 ], but it is no less true that students were forced to rely much more on their own resources to sustain the continuity of their learning during this period [ 11 – 13 ]. In the particular case of Argentina the confinement measures began on March 20, 2020, affected all educational levels and coincided with the beginning of the first semester of the academic year.

The new educational context not only brought about great challenges but was also reflected in the results obtained by the students [ 14 – 18 ]. The effects of the change in the learning conditions, although recent and still in process, have been analyzed from different perspectives [ 11 – 17 ]. A less explored methodology, which we propose to address here, is to study this problem from the point of view of complex systems, in line with what was done by some of the authors of this work just before the start of the pandemic [ 19 ]. The reason behind choosing this research design lies in the fact that the approach from this perspective allows the interactions between the individuals involved to be adequately considered when analyzing the effect of a global variable, such as the pandemic. But in addition, the usefulness of mathematical modeling to unravel the relevance of different factors that are present in the knowledge acquisition process was demonstrated in our previous work.

In [ 19 ] we developed an analytical model (the KA model) based on data from a series of surveys that are contrasted with information on academic performance of students, to analyze how the knowledge acquisition depends globally on different extrinsic and intrinsic factors. Regarding the intrinsic factors, one that contributes greatly to the acquisition of knowledge of students is motivation, and this is precisely one of the most affected by the pandemic [ 20 ]. According to the EU report [ 14 ], the closure of physical schools and the adoption of distance education can negatively affect student learning through four main channels: less time spent learning, symptoms of stress, a change in the way that students interact, and lack of motivation to learn. But, it is possible to use a model to assess the hypothesis that lack of motivation is one of the strongest negative impacts of the pandemic on students, regardless of their personal characteristics? In particular, and since the KA model was developed for a specific (face-to-face) context, the first question to be answered in this work should be whether this model is sensitive to modifications of the educational context.

On the other hand, it was already mentioned that the change in physical context affected extrinsic factors that contribute to the acquisition of knowledge, such as the interaction with peers and teachers. This interaction has been found to be essential for the development of positive self-esteem, self-confidence, and a sense of identity. In fact, there is significant evidence showing that social skills are positively associated with cognitive skills and school achievement [ 21 , 22 ]. In this regard, a series of questions arise: From the perspective of the students, did the bond with teachers improve or worsen during the pandemic? Did the interaction between peers change with the change of context? What aspects of it can be measured in the new context?

Analyzing the consequences of the pandemic on the educational performance is a matter of global importance. It is well known that the distance education is essential to ensure the continuity of learning in situations in which face-to-face classes are suspended. In places where virtual and remote strategies were already becoming a reality, the change was a positive [ 18 ]. However, in other countries something as basic as Internet access is still a privilege, guaranteeing distance education cannot be taken for granted. The preparation (or lack thereof) of some countries in this area has revealed the weaknesses of educational methodologies and resources [ 13 ]. Bringing this situation to light is one more step towards fairness.

The previous statements prompt us to seek answers about how much the academic performance of students was affected by the change in the educational context caused by the pandemic. In addition, and in relation to the KA model, we would like to evaluate whether the aspects that we consider relevant have a comparable importance in the construction of knowledge, as well as the consistency of these results when comparing both scenarios.

In this new approach we adapt the analytical model presented in [ 19 ] to compare the knowledge acquisition process in two different contexts: face-to-face (before the onset of the pandemic) and virtual (during the confinement), for a particular case in higher education in Argentina. We present a study that involves 173 students and its entire evolution during 2019 and 2020 in both contexts. Furthermore, and in order to assess the relevance of the parameters we chose for our model, we apply two robust and versatile tools used in multiple applications: Artificial Neural Networks and a Multiple Linear Regression Method.

The article is organized as follows: in the Methods section we describe the participants and its educational context, the data collection and variables (which include the surveys used to construct our data-based model) and the different approaches used to fit the parameters of the model. Then, we present the main results of this work and finally, we summarize and discuss our findings.

Educational context

The research was carried out with several sections of students who attended the Physics II course, corresponding to the second year of Engineering careers at the Faculty of Engineering of the National University of La Plata (UNLP) [ 23 ], Argentina, during the years 2019 and 2020. The Faculty offers 13 engineering degrees, so the interest of the students in the course can vary greatly.

The complete course lasts one semester, with a workload of 8 hours per week divided into 2 theoretical-practical classes. The course consists of two parts, at the end of which a partial written test is taken with a score between 0 and 10. There are two approval regimes: direct promotion, which implies being exempt from the final test (if the average between the two partial exams is 6 or more) or promotion by final exam (if the average is between 4 and 6). Partial tests have an instance of recuperation during the semester and another at the end of it, where the student can improve any of the lower scores obtained in previous tests. This organization was also maintained during the confinement (in virtual context).

Participants

The first part of the research was done during the two semesters of the year 2019, with four different sections in face-to-face context for a total of 81 students (50 male, 31 female). The second part was developed during the year 2020 and also involved four different sections in two semesters, for a total of 92 students (61 male, 31 female). In all cases we had access to the final grade they obtained in the course. In both contexts, we worked with 4 different sections of students for a total of 8 sections, 173 students in 2 years. The initial group of students was much larger, however there were 173 who participated in the whole process. These data are reported in the ( S1 File ) and has been collected with the following actions:

• It does not involve minors.
• It has been collected anonymously. Students have been identified by a numerical code, avoiding gathering of any personal information.
• Students have been informed by the lecturers that some information about their activity could be anonymously collected for statistical purposes. Authors of this study did not receive any objections.
• The tasks related to this study were completely voluntary and they did not in any form alter students’ activities, classes, or the assessment process.

Considering these circumstances, we do not need to apply for ethics approval from our university since no personal data, minors or potentially hazardous activities were involved in the study.

Besides, all teachers involved in the study (some of them also co-authors of this manuscript) who were responsible for the subject taught also gave consent to carry out the study.

We obtained verbal consent from all the participants in the study.

Data collection and variables

We are interested in analyzing and comparing the processes observed in both contexts in terms of the KA model presented in [ 19 ]. A first step consisted in carrying out a classification such as that proposed by Bordogna and Albano [ 24 ] and which proved to be useful in our previous work. This involved separating the students into three different groups according to their final achievements K f , which we relate to the final grade obtained in the course. This was done as follows: (a) High-achieving (HA) students: 8 ≤ K f ≤ 10, (b) Average-achieving (AA) students: 6 < K f < 8 and (c) Low-achieving (LA) students: K f ≤ 6. It is worth noting that students with a final grade lower than 4 are not included in this study.

In Table 1 we show the number of students who participated in the work divided according to their final achievements K f , that we relate to the final grade obtained in the course. Interestingly, and as we found in [ 19 ], the groups have qualitatively different characteristics regarding the relevance of the factors considered in the construction of the new knowledge, as it will be clear shortly.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0274039.t001

In Fig 1 we display the final grades obtained for all students that we include in the present work. In filled symbols we plot the data in the face-to-face context and the empty symbols represent the data in the virtual context. These data provide us with the information to contrast our theoretical model. A first look at this graph reveals that the marks obtained in the two contexts were different for the HA and LA groups, while the AA group did not present differences. HA students, whose grades were higher than 8, had on average a better performance in virtual context than in face-to-face context. The opposite is seen with the Low-achievement students, LA. To analyze the possible causes of these differences is one of the main purposes of the present paper.

https://doi.org/10.1371/journal.pone.0274039.g001

KA model for both contexts.

It is worth noting that in our study the contribution of peers to the acquisition of knowledge was gathered in two ways: the group conformation and the peer interaction itself. The group conformation includes information on the spatial distribution of the students and the formation of groups, obtained through direct observations of the classes before confinement and through questions in online surveys during confinement. An analysis of the differences in the structure of the peer network formed in each context is carried out in Fig 4 in the Results section.

During the virtual context, important and complementary information was also collected, such as resources the students had (work-space, technological equipment) and the context itself and how it was perceived. Although they are not included as terms in Eq 1 , we carry out a description of the observed situation in the S2 File .

Finally, it should be noted that in our study we focus on a specific type of learning, related to scientific concepts of classical physics. While we are aware that this is not the only value learned in the classroom, we simplify the concept of knowledge to use the final grade as a concrete and quantifiable measure of the student’s performance.

Here we present the surveys carried out on students during each semester of classes ( Table 2 ). The numbers and letters in the last column correspond to the values that we assign to each of them, in order to transfer the answers to the KA model of Eq 1 . The questions marked with (*) were reformulated to adapt them to the virtual context. The surveys carried out in the virtual context were delivered and completed in a digital way using Google tools, while those corresponding to the pre-confinement stage were delivered personally and were completed manually.

Although the surveys were broader, here we only include the questions involved in the model.

https://doi.org/10.1371/journal.pone.0274039.t002

The quantities evaluated more than once (as is the case of M or T ) were averaged in order to have a single value for each factor. Besides, the combination of strategies for the question that measures the interaction with the teacher T in the third survey was given the following numerical values: ABC = AB = AC = BC = 1, A = B = 0.7, C = AD = BD = ABD = ACD = BCD = 0.5, CD = 0.3, D = 0.1 (students could mark several options). These values were given to enhance the use of the strategies provided by the specific section to which the students belonged (options A, B).

From surveys to KA model.

https://doi.org/10.1371/journal.pone.0274039.t003

Proposed tools for analysis

As it was already mentioned, each of these groups has different characteristics regarding the relevance of the factors considered in the construction of Eq 1 . To explicitly measure the weight of each of them we apply two different and complementary approaches: Artificial Neural Networks (ANN) and a Multiple Linear Regression Method (MLR).

In reference [ 25 ], the capability of the ANN for estimating parameters of complex nonlinear and linear problems has been shown. A single-layer perceptron (SLP) constitutes a particular case of the ANN whose output equation resembles Eq 1 . This allows to cross-validate the MLR, which is the most common form of linear regression analysis to treat this kind of problem.

Single Layer Perceptron (SLP) network overview.

To reproduce Eq 1 from an ANN architecture we employed a SLP [ 25 ]. This type of ANN constitutes a particular case of a Multilayer Perceptron (MLP) [ 26 ]. The SLP is a feedforward network of a single artificial neuron-like unit, whose x j inputs (disposed akin to biologic dendrites) are multiplied by a corresponding weight w j and this product is passed to a neuron-like unit where the aforementioned product is added up, as shown in Fig 2 .

The usual ANN notation is in black text and in red text, the equivalent terms corresponding to this particular work are shown. (See Eq 1 ). The element-wise product between the inputs and the weights are added up in the “net input function” stage and suppressing the activation function, an output corresponding to the linear combination of the inputs and the weights is obtained.

https://doi.org/10.1371/journal.pone.0274039.g002

The SLP model was implemented in the programming language Python by means of the Keras package [ 27 ].

Multiple Linear Regression Method.

Besides, β M , β T , β P , β HA , β LA and β F are the regression coefficients corresponding to the variables M , T , P , HA , LA and F , respectively, and they were estimated through the OLS (Ordinary Least Squares) method.

This model was fitted using the function lm() in the programming language R version 4.1.0 [ 28 ].

Comparison between contexts

In our previous work [ 19 ], we compared the results of our KA model with the final grade that the students obtained. Looking for an answer to our main question, about how the educational context affected student performance, we first compare the general results in both, face-to-face and virtual contexts.

We proposed in Eq 1 that the final knowledge reached by a student on a given topic is mainly due to three contributing factors, the personal motivation ( M ), the influence of the teachers ( T ) and the influence of peers ( P ). In Fig 3 we show the average values of the final grade of each group, < K f >, together with average of the data obtained from the surveys carried out, in order to analyze and compare the differences observed with the change of context.

(a) Final grade < K f >, (b) motivation M , (c) interaction with teachers T and (d) interaction between peers P .

https://doi.org/10.1371/journal.pone.0274039.g003

These results allow us to respond positively to our first question, about whether our approach is sensitive to changes in the educational context. As we can see, although the KA model was originally developed for a specific context (face to face), the values of the variables are different for both contexts.

In Fig 3(a) we show the average final grade < K f > for each group of students (HA, AA and LA) and in both contexts. We observe again the differences we first noticed in Fig 1 , related to how the performance of each group is modified with the change of context. For HA students, < K f > increased during the virtual context while for AA and LA it decreased. In what follows, and to deepen the understanding of what is observed, we will analyze what was obtained for the three contributing factors (also averaged for each group), and that we plot in panels (b), (c) and (d) of Fig 3 .

The values of motivation presented in Fig 3(b) reflects a widely studied aspect of the psychological impact of the pandemic on students [ 14 , 20 ]. Our results clearly report the impact of the virtual context on the motivation of students, no matter the group they belong to. This fact should in itself be an alarm to build policies to support the mental health and educational success of the students at all times. If motivation dropped notably in the new virtual context, and the final knowledge is considered as the sum of several factors that contribute to the acquisition of this knowledge, then the way of interacting with peers and teachers also had to change.

The general decrease in the virtual context observed in motivation is not repeated in the other factors analyzed in this study. Fig 3(c) gives us information about the teacher’s contribution from the students’ perspective. Note that for the HA group it has the same weight in both contexts (face-to-face and virtual), while for the AA and LA groups the interaction with teachers increased in the virtual context. Generally, the teacher acts as an intermediary between the activities carried out by the students in order to assimilate the new knowledge and in this new context their presence and support (albeit virtual) was fundamental for many students.

Finally, in Fig 3(d) , we can see the differences in the interaction between peers for each group of students, another issue that was affected during the pandemic.

We can see that HA’s enriched the study in groups in the virtual context in contrast to the other groups of students. We also found that the structure of the emerging contact network from peer interaction presents very different characteristics in both contexts. More details about this aspect of the problem are presented in the next subsection. The situation observed in Fig 3(d) for the interaction between peers is the one that most reflects the behavior of the general performance ( Fig 3(a) ), however the trend is attenuated due to what is observed in Fig 3(b) and 3(c) . These results may partially respond to the change observed in the way students interact.

The aforementioned results can be summarized in Table 4 where we show the relative changes between both contexts. This quantity expresses what it was observed in Fig 3 with the raw data obtained in the surveys: A strong decrease in the motivation term for all groups of students, and different trends in the way of interacting with peers and with teachers depending on the group to which the students belong.

https://doi.org/10.1371/journal.pone.0274039.t004

Networks of peer interactions.

The analysis carried out around Fig 3 indicates that the change in physical context modified the way in which students interact with each other. Furthermore, in this area data was collected in different ways depending on the context. In the face-to-face context, the observations in the classroom were made in situ, with photographic records and paper surveys. During the virtual context, the surveys were digital using Google tools as mentioned above. In the latter case, no observations could be made, so the students were asked how their interaction with the group was and with whom they specifically interacted. This fact could result in a lack of information for this context. However, that was not the case, since although the information collected in both cases is not completely comparable, they suggest a change in behavior in the relationship between peers. Table 5 expresses the number of students who were observed grouped or isolated during the face-to-face classes. Likewise, for the virtual case, the number of students who affirmed to study or not in a group is reported. We find that the percentage of isolated students decreased from 37% to 26% with the change of context. Interestingly, the increase in interaction between students in the virtual context was observed to a greater or lesser extent for the three groups.

https://doi.org/10.1371/journal.pone.0274039.t005

To deepen the understanding of how students modified their way of interacting, we draw in Fig 4(a) the network that represents the students before confinement (face-to-face context) for N = 81. As we said, the data was obtained from direct observations in the classroom, where the nodes represent the students (divided in the HA, AA and LA groups) and the links their interactions. Note that here we use double bonds, indicating a reciprocal interaction.

(a) Network scheme from classroom observations in face-to-face context where the links are reciprocal interactions. (b) Network scheme from data obtained through surveys in virtual context. The links can be or not be reciprocal interactions. The nodes marked with an asterisk represent students who claimed to interact with students from another section who did not participate in this study.

https://doi.org/10.1371/journal.pone.0274039.g004

Besides, in Fig 4(b) we show the network that describes the students in virtual context for N = 92. The data come from the surveys carried out, and again the nodes represent the students divided in the groups HA, AA and LA. We use links to represent their interactions, although now they are double or single, as the responses to the surveys given by the students may or may not be reciprocal. Moreover, the nodes marked with an asterisk represent students who claimed to interact with students from another section who did not participate in this study.

A comparison between both networks indicates some similarities, such as the presence of highly connected clusters, as well as isolated students. However, the network corresponding to the virtual context has nodes that connect two different clusters, acting as “bridges”. This was not observed in the face-to-face context and could mean a new form of relationship between students. This result deepens the understanding of the effect that the pandemic has on peer relationships, and answers some of the questions asked in the introduction on this topic.

Measure of the relevance of the terms that influence the knowledge acquisition process

A way to validate the model presented in Eq 1 is to analyze the relevance of the terms that compose it. In our previous work [ 19 ] we did it by adding coefficients to each factor of the KA model. These coefficients could be interpreted as the relative weight that each term in Eq 1 has, and were chosen so that the average value calculated with the model for each group is as close as possible to the average value of the actual final grades obtained. In order to analyze the relevance and consistency of the factors that we chose to describe the knowledge acquisition process, we now we choose two different and complementary approaches to find the weight of each term of Eq 1 : Artificial Neural Networks (ANN) and a Multiple Linear Regression Method (MLR).

ANN approach.

https://doi.org/10.1371/journal.pone.0274039.g005

https://doi.org/10.1371/journal.pone.0274039.t006

Finally, in Fig 6 we present a comparison between the final grade for each student and the final knowledge obtained from Eq 1 (KA model) with the coefficients obtained with the ANN approach. The global behavior of the KA model follows the general trend of the data. The observed dispersion is due to the presence of particular cases, whose complete evolution is not captured by the model. In our previous work [ 19 ] we made an analysis of some particular cases like these.

https://doi.org/10.1371/journal.pone.0274039.g006

MLR approach.

We make use of the Multiple Linear Regression Method in order to find the weights of each contributing factor of the KA model, and compare them with the ones obtained in the previous section. The results are shown in Table 7 , where we express the values for β , SE (standard error) and p-value for the terms of the Eq 3 .

https://doi.org/10.1371/journal.pone.0274039.t007

The p-values obtained show that all beta regression coefficients are statistically significant. Assumptions of linearity, independence, homoscedasticity and normality were checked, as well as the presence of influential values.

At last, we show in Fig 7 a comparison between the final grade for each student and the final knowledge of Eq 1 (KA model) with the coefficients obtained with the MLR approach. Again, the K f obtained with the model behaves similarly to the data. It should be noted the similarity of the result obtained in Figs 5 and 6 with that shown in Fig 2 of [ 19 ]. In the present work, the adjustment of the weights that gave rise to both figures was carried out in a more appropriate way than in that paper, where the coefficients of each term were chosen exploratory.

https://doi.org/10.1371/journal.pone.0274039.g007

In a previous work we proposed to describe the knowledge acquisition process as a dynamic quantity composed of several terms, where it was implicit that such a process was carried out in the classroom. But, what happens when the physical place where this complex socio-cultural construction takes place changes? What are the consequences of that specific educational context being taken from one day to the next? We seek to answer these questions by discussing how the terms of the knowledge acquisition model were modified, and which ones most directly influenced student performance during the transition to virtuality.

For that, we analyze the knowledge acquisition process in face-to-face and virtual contexts for a specific study case. Our investigation spanned two years and involved 173 students, observing the evolution of their learning process for each particular context.

Inspired by the work of Ref. [ 19 ], we wanted to assess whether the observed changes in academic performance can be understood from a model that incorporates the main factors that contribute to the knowledge acquisition process. The KA model is an analytical model based on data, which incorporates information from a series of surveys and whose results are contrasted with information on academic performance. The surveys were carried out 3 times during each semester and reflected the feelings of the students during their learning process that influenced their performance.

The raw data in Fig 1 show that the final grade of the students in both contexts presented differences. Specifically, the grades of the students with High-achievement (HA) were better in virtual context than in face-to-face context. The opposite is seen with Low-achieving students (LA), while the intermediate performance group (AA) did not show differences. In the results shown in [ 18 ], the performance of the students who used remote learning tools showed an improvement in the virtual context. We believe that this difference is due to the fact that having better resources positioned them in a privileged place with respect to the case studied in this work.

The results obtained in Fig 3 reinforce accepted ideas related to the importance of motivation in the learning process: the switch to virtual context caused a negative impact on the motivation of the entire student population, but was strongly reflected in the performance of LA students. This fact should alert the educational community and especially those responsible for building support mechanisms for the mental health of students. Furthermore, we observe that the new context generates a change in the way students interact with their peers and teachers. In particular, the HA students did not modify the interaction with the teachers (maintaining high values in both contexts) while they strengthened the study in groups in the virtual context, unlike the rest of the groups. For AA and LA students, interaction with teachers increased in the virtual context, and this result highlights the importance of the teacher’s role as a consultant and as fundamental support for students.

We also find that the structure of the network of contacts that is formed between peers in both contexts presents some common characteristics, as well as some interesting differences, as we saw in Fig 4 . Among the first is that both networks have highly connected clusters, as well as a significant number of completely isolated students. The virtual context network, however, shows a feature not observed in the other network: the presence of individuals who interact with one or more students from different clusters. These individuals act as bridges between students who otherwise would not be connected. These structures could be reflecting a new form of relationship between students that occurs more easily in the virtual context. Nevertheless, we are aware that this analysis requires a more detailed investigation that is beyond the scope of this work with the data we currently have. On the other hand, it is also true that the virtual context made it possible to record that the interactions between the students go beyond what happens in the classroom space.

Related with the previous analysis is the fact that, although the equation in the KA model is linear, the term of peers can be interpreted as an effective version of a real non-linear interaction. This term in itself adds complexity to the model since group interaction does not obey “linear” rules. However, the simplification made in the KA model remains valid in light of the results obtained in [ 19 ] and are in line with the idea that the learning process is not limited to the interactive behavior of individual teachers and students, but should be understood in terms of collaborative behavior [ 29 ].

In order to find out the relevance of the factors that we included in the KA model, we used two different approaches: a standard Multiple Linear Regression Method and a Single Layer Perceptron, which is a particular type of Artificial Neural Network.

The results obtained with the neural network ( Fig 5 ) indicate that in both contexts the weights are similar. This result also shows that the raw results adequately describe each context, since the data obtained in each situation reflect the particular reality that each group of students is going through.

Moreover, both approaches indicate a greater relevance of the term of interaction with teachers. We were able to collect information from the teachers to support this fact and the perception of the change in the interaction with the students was also commented on by them (see S2 File ). The knowledge acquisition process comes hand in hand with the importance of the interaction with teachers, and the literalness of their presence in the accompaniment during learning. This result also confirms in some way the universality of the educational act.

The comparisons of Figs 6 and 7 between the raw data and the results obtained with the KA model indicate that the general behavior of individuals can be suitably described with Eq 1 , which is simply the sum of the relative contributions of each of the proposed factors: personal motivation, interaction with peers and influence of teachers. The robustness of the coefficients obtained with the two approaches also indicates that the information collected in the surveys and observations was sufficient to construct an adequate representation of the process. We are aware that this simplification leaves out a huge number of variables that are integrated to give rise to the unique process that each person experiences. But we believe that the results obtained allow us to validate our choice of factors as the main contributions common to all individuals.

Now, we discuss some considerations on the scope and limitations of this work.

One is that we must not lose sight of the fact that the change in the specific physical context brought with it a change in the evaluation criteria. Actually, this aspect was addressed in the teacher interviews that we summarize in the ( S2 File ). As K f is a hard data (the final grade obtained in the course), it would be more appropriate to build new models that consider these data in a more comprehensive way, taking into account the challenges that arose due to the change in this educational context.

Another important issue that is absent from the KA model is the personal context of the students and their available resources. The reason why it was not included is because we had no survey done on these topics in the face-to-face period, so it was not possible to compare both contexts. However, in the Supplementary Material ( S2 File ) we include additional information regarding this subject obtained from the surveys carried out in the virtual context. When asking the students for their feelings regarding confinement, the responses were varied but reluctance was reflected in more than half of the responses. This coincides with our observation about the lack of motivation (see Fig 3(b) ). The emotional stress, widely discussed in this context, goes beyond the academic environment and it was an important characteristic that we tried to capture with our research. Moreover, we found some relevant differences between the students of the different groups, which could influence their performance. Among them, a third of the students belonging to the LA group said they had a poor Internet connection in contrast to the HA group in which this situation occurred for a sixth of the students. More importantly, 13% of students belonging to the LA group did not have a laptop computer and 30% did not have an adequate study space.

These results show how the pandemic has increased educational inequalities at the economic, technological, social and even emotional level of the actors in the educational process. The virtual context promoted a change in teaching and learning methodologies, but it also brought another great challenge that is still far from being resolved, namely access to resources for all students. Hence the importance of recognizing inequalities to make visible the urgent need to build university policies that improve this situation.

A final though has to do with the generalizability of our results. Although this study was done for a specific case, the main factors analyzed here (motivation, interaction with peers and teachers) are not isolated from the global scenario. The generalization of the KA model to other educational scenarios is not only possible but quite straightforward. It should be noted, however, that the part of our study referring to the virtual context was carried out during the first year of the pandemic, so the results obtained could be strongly influenced by the transition between both contexts. Nevertheless, we believe they are valuable in themselves and can serve to deepen the understanding of the complex process of learning.

Supporting information

S1 file. survey data: numerical values associated with the ka model..

https://doi.org/10.1371/journal.pone.0274039.s001

S2 File. Additional information obtained from student and teacher surveys.

https://doi.org/10.1371/journal.pone.0274039.s002

Acknowledgments

The authors acknowledge Dr. José Javier Ramasco for his helpful suggestions on data analysis and availability.

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The pandemic has had devastating impacts on learning. What will it take to help students catch up?

Subscribe to the brown center on education policy newsletter, megan kuhfeld , megan kuhfeld senior research scientist - nwea @megankuhfeld jim soland , jim soland assistant professor, school of education and human development - university of virginia, affiliated research fellow - nwea @jsoland karyn lewis , and karyn lewis director, center for school and student progress - nwea @karynlew emily morton emily morton research scientist - nwea @emily_r_morton.

March 3, 2022

As we reach the two-year mark of the initial wave of pandemic-induced school shutdowns, academic normalcy remains out of reach for many students, educators, and parents. In addition to surging COVID-19 cases at the end of 2021, schools have faced severe staff shortages , high rates of absenteeism and quarantines , and rolling school closures . Furthermore, students and educators continue to struggle with mental health challenges , higher rates of violence and misbehavior , and concerns about lost instructional time .

As we outline in our new research study released in January, the cumulative impact of the COVID-19 pandemic on students’ academic achievement has been large. We tracked changes in math and reading test scores across the first two years of the pandemic using data from 5.4 million U.S. students in grades 3-8. We focused on test scores from immediately before the pandemic (fall 2019), following the initial onset (fall 2020), and more than one year into pandemic disruptions (fall 2021).

Average fall 2021 math test scores in grades 3-8 were 0.20-0.27 standard deviations (SDs) lower relative to same-grade peers in fall 2019, while reading test scores were 0.09-0.18 SDs lower. This is a sizable drop. For context, the math drops are significantly larger than estimated impacts from other large-scale school disruptions, such as after Hurricane Katrina—math scores dropped 0.17 SDs in one year for New Orleans evacuees .

Even more concerning, test-score gaps between students in low-poverty and high-poverty elementary schools grew by approximately 20% in math (corresponding to 0.20 SDs) and 15% in reading (0.13 SDs), primarily during the 2020-21 school year. Further, achievement tended to drop more between fall 2020 and 2021 than between fall 2019 and 2020 (both overall and differentially by school poverty), indicating that disruptions to learning have continued to negatively impact students well past the initial hits following the spring 2020 school closures.

These numbers are alarming and potentially demoralizing, especially given the heroic efforts of students to learn and educators to teach in incredibly trying times. From our perspective, these test-score drops in no way indicate that these students represent a “ lost generation ” or that we should give up hope. Most of us have never lived through a pandemic, and there is so much we don’t know about students’ capacity for resiliency in these circumstances and what a timeline for recovery will look like. Nor are we suggesting that teachers are somehow at fault given the achievement drops that occurred between 2020 and 2021; rather, educators had difficult jobs before the pandemic, and now are contending with huge new challenges, many outside their control.

Clearly, however, there’s work to do. School districts and states are currently making important decisions about which interventions and strategies to implement to mitigate the learning declines during the last two years. Elementary and Secondary School Emergency Relief (ESSER) investments from the American Rescue Plan provided nearly $200 billion to public schools to spend on COVID-19-related needs. Of that sum, $22 billion is dedicated specifically to addressing learning loss using “evidence-based interventions” focused on the “ disproportionate impact of COVID-19 on underrepresented student subgroups. ” Reviews of district and state spending plans (see Future Ed , EduRecoveryHub , and RAND’s American School District Panel for more details) indicate that districts are spending their ESSER dollars designated for academic recovery on a wide variety of strategies, with summer learning, tutoring, after-school programs, and extended school-day and school-year initiatives rising to the top.

Comparing the negative impacts from learning disruptions to the positive impacts from interventions

To help contextualize the magnitude of the impacts of COVID-19, we situate test-score drops during the pandemic relative to the test-score gains associated with common interventions being employed by districts as part of pandemic recovery efforts. If we assume that such interventions will continue to be as successful in a COVID-19 school environment, can we expect that these strategies will be effective enough to help students catch up? To answer this question, we draw from recent reviews of research on high-dosage tutoring , summer learning programs , reductions in class size , and extending the school day (specifically for literacy instruction) . We report effect sizes for each intervention specific to a grade span and subject wherever possible (e.g., tutoring has been found to have larger effects in elementary math than in reading).

Figure 1 shows the standardized drops in math test scores between students testing in fall 2019 and fall 2021 (separately by elementary and middle school grades) relative to the average effect size of various educational interventions. The average effect size for math tutoring matches or exceeds the average COVID-19 score drop in math. Research on tutoring indicates that it often works best in younger grades, and when provided by a teacher rather than, say, a parent. Further, some of the tutoring programs that produce the biggest effects can be quite intensive (and likely expensive), including having full-time tutors supporting all students (not just those needing remediation) in one-on-one settings during the school day. Meanwhile, the average effect of reducing class size is negative but not significant, with high variability in the impact across different studies. Summer programs in math have been found to be effective (average effect size of .10 SDs), though these programs in isolation likely would not eliminate the COVID-19 test-score drops.

Figure 1: Math COVID-19 test-score drops compared to the effect sizes of various educational interventions

Source: COVID-19 score drops are pulled from Kuhfeld et al. (2022) Table 5; reduction-in-class-size results are from pg. 10 of Figles et al. (2018) Table 2; summer program results are pulled from Lynch et al (2021) Table 2; and tutoring estimates are pulled from Nictow et al (2020) Table 3B. Ninety-five percent confidence intervals are shown with vertical lines on each bar.

Notes: Kuhfeld et al. and Nictow et al. reported effect sizes separately by grade span; Figles et al. and Lynch et al. report an overall effect size across elementary and middle grades. We were unable to find a rigorous study that reported effect sizes for extending the school day/year on math performance. Nictow et al. and Kraft & Falken (2021) also note large variations in tutoring effects depending on the type of tutor, with larger effects for teacher and paraprofessional tutoring programs than for nonprofessional and parent tutoring. Class-size reductions included in the Figles meta-analysis ranged from a minimum of one to minimum of eight students per class.

Figure 2 displays a similar comparison using effect sizes from reading interventions. The average effect of tutoring programs on reading achievement is larger than the effects found for the other interventions, though summer reading programs and class size reduction both produced average effect sizes in the ballpark of the COVID-19 reading score drops.

Figure 2: Reading COVID-19 test-score drops compared to the effect sizes of various educational interventions

Source: COVID-19 score drops are pulled from Kuhfeld et al. (2022) Table 5; extended-school-day results are from Figlio et al. (2018) Table 2; reduction-in-class-size results are from pg. 10 of Figles et al. (2018) ; summer program results are pulled from Kim & Quinn (2013) Table 3; and tutoring estimates are pulled from Nictow et al (2020) Table 3B. Ninety-five percent confidence intervals are shown with vertical lines on each bar.

Notes: While Kuhfeld et al. and Nictow et al. reported effect sizes separately by grade span, Figlio et al. and Kim & Quinn report an overall effect size across elementary and middle grades. Class-size reductions included in the Figles meta-analysis ranged from a minimum of one to minimum of eight students per class.

There are some limitations of drawing on research conducted prior to the pandemic to understand our ability to address the COVID-19 test-score drops. First, these studies were conducted under conditions that are very different from what schools currently face, and it is an open question whether the effectiveness of these interventions during the pandemic will be as consistent as they were before the pandemic. Second, we have little evidence and guidance about the efficacy of these interventions at the unprecedented scale that they are now being considered. For example, many school districts are expanding summer learning programs, but school districts have struggled to find staff interested in teaching summer school to meet the increased demand. Finally, given the widening test-score gaps between low- and high-poverty schools, it’s uncertain whether these interventions can actually combat the range of new challenges educators are facing in order to narrow these gaps. That is, students could catch up overall, yet the pandemic might still have lasting, negative effects on educational equality in this country.

Given that the current initiatives are unlikely to be implemented consistently across (and sometimes within) districts, timely feedback on the effects of initiatives and any needed adjustments will be crucial to districts’ success. The Road to COVID Recovery project and the National Student Support Accelerator are two such large-scale evaluation studies that aim to produce this type of evidence while providing resources for districts to track and evaluate their own programming. Additionally, a growing number of resources have been produced with recommendations on how to best implement recovery programs, including scaling up tutoring , summer learning programs , and expanded learning time .

Ultimately, there is much work to be done, and the challenges for students, educators, and parents are considerable. But this may be a moment when decades of educational reform, intervention, and research pay off. Relying on what we have learned could show the way forward.

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Here's how COVID-19 affected education – and how we can get children’s learning back on track

Nearly 147 million children missed more than half of their in-person schooling between 2020 and 2022. Image:  Unsplash/Taylor Flowe

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Stay up to date:, global health.

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• As well as its health impacts, COVID-19 had a huge effect on the education of children – but the full scale is only just starting to emerge.
• As pandemic lockdowns continue to shut schools, it’s clear the most vulnerable have suffered the most.
• Recovering the months of lost education must be a priority for all nations.

When the World Health Organization declared COVID-19 to be a pandemic on 11 March 2020, few could have foreseen the catastrophic effects the virus would have on the education of the world’s children.

During the first 12 months of the pandemic, lockdowns led to 1.5 billion students in 188 countries being unable to attend school in person, causing lasting effects on the education of an entire generation .

As an OECD report into the effects of school closures in 2021 put it: “Few groups are less vulnerable to the coronavirus than school children, but few groups have been more affected by the policy responses to contain the virus.”

Although many school closures were announced as temporary measures, these shutdowns persisted throughout 2020 – and even beyond in some cases.

As late as March 2022, UNICEF reported that 23 countries, home to around 405 million schoolchildren, had not yet fully reopened their schools . As China battled to contain new COVID-19 outbreaks, schools were closed in Shanghai and Xian in October 2022.

COVID has ended education for some

Nearly 147 million children missed more than half of their in-person schooling between 2020 and 2022, UNICEF says. And it warns that many, especially the most vulnerable, are at risk of dropping out of education altogether.

The danger is highlighted by UNICEF data showing that 43% of students did not return when schools in Liberia reopened in December 2020. The number of out-of-school children in South Africa tripled from 250,000 to 750,000 between March 2020 and July 2021, UNICEF adds.

When schools in Uganda reopened after being closed for two years, almost one in ten children were missing from classrooms. And in Malawi, the dropout rate among girls in secondary education increased by 48% between 2020 and 2021.

Out-of-school children are among the most vulnerable and marginalized children in society, says UNICEF. They are the least likely to be able to read, write or do basic maths, and when not in school they are at risk of exploitation and a lifetime of poverty and deprivation, it says.

Lost learning time

Even when children are in school, the amount of learning time they have lost to the pandemic is compounding what UNICEF describes as “a desperately poor level of learning” in 32 low-income countries it has studied.

“In the countries analyzed, the current pace of learning is so slow that it would take seven years for most schoolchildren to learn foundational reading skills that should have been grasped in two years, and 11 years to learn foundational numeracy skills,” the charity says.

Analysis of the crisis by UNESCO, published in November 2022, found that the most vulnerable learners have been hardest hit by the lack of schooling. It added that progress towards the United Nations Sustainable Development Goal for Education had been set back.

In Latin America and the Caribbean – a region that suffered one of the longest periods of school closures – average primary education scores in reading and maths could have slipped back to a level last seen 10 years ago , the World Bank says.

Four out of five sixth graders may not be able to adequately understand and interpret a text of moderate length, the bank says. As a result, these students are likely to earn 12% less over their lifetime than if their education had not been curtailed by the pandemic, it estimates.

Widening the achievement gap

In India, the pandemic has widened the gaps in learning outcomes among schoolchildren with those from disenfranchised and vulnerable families falling furthest behind, according to a 2022 report by the World Economic Forum.

Even where schools tried to keep teaching using remote learning, the socio-economic divide was perpetuated. In the United States, a study found children’s achievement in maths fell by 50% more in less well-off areas , compared to those in more affluent neighbourhoods.

One year on: we look back at how the Forum’s networks have navigated the global response to COVID-19.

Using a multistakeholder approach, the Forum and its partners through its COVID Action Platform have provided countless solutions to navigate the COVID-19 pandemic worldwide, protecting lives and livelihoods.

Throughout 2020, along with launching its COVID Action Platform , the Forum and its Partners launched more than 40 initiatives in response to the pandemic.

The work continues. As one example, the COVID Response Alliance for Social Entrepreneurs is supporting 90,000 social entrepreneurs, with an impact on 1.4 billion people, working to serve the needs of excluded, marginalized and vulnerable groups in more than 190 countries.

Read more about the COVID-19 Tools Accelerator, our support of GAVI, the Vaccine Alliance, the Coalition for Epidemics Preparedness and Innovations (CEPI), and the COVAX initiative and innovative approaches to solve the pandemic, like our Common Trust Network – aiming to help roll out a “digital passport” in our Impact Story .

Consultancy firm McKinsey says that US students were on average five months behind in mathematics and four months behind in reading by the end of the 2020-21 school year. Disadvantaged students were hit hardest, with Black students losing six months of learning on average.

Researchers in Japan found a similar pattern, with disadvantaged children and the youngest suffering most from school closures. They said the adverse effects of being forced to study at home lasted longest for those with poorest living conditions .

However, in Sweden, where schools stayed open during the pandemic, there was no decline in reading comprehension scores among children from all socio-economic groups, leading researchers to conclude that the shock of the pandemic alone did not affect students’ performance.

Getting learning back on track

So what can be done to help the pandemic generation to recover their lost learning ?

The World Bank outlines 10 actions countries can take, including getting schools to assess students’ learning loss and monitor their progress once they are back at school.

Catch-up education and measures to ensure that children don’t drop out of school will be essential, it says. These could include changing the school calendar, and amending the curriculum to focus on foundational skills.

There’s also a need to enhance learning opportunities at home, such as by distributing books and digital devices if possible. Supporting parents in this role is also critical, the bank says.

Teachers will also need extra help to avoid burnout, the bank notes. It highlights a “need to invest aggressively in teachers’ professional development and use technology to enhance their work”.

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Vol. 25 No. 1 (2024): Current Issues in Education’s Spring Issue

Welcome to the Spring issue of Current Issues in Education, where we embark on a journey through the dynamic landscape of contemporary educational research. In this edition, we are delighted to present a collection of insightful papers that delve into critical topics shaping the field of education today.

As we navigate the complexities of education, one recurring theme that emerges from our exploration is the pursuit of equity and social justice. From examining the limitations in education in regards to developing the possible selves of young Black men through Hip Hop-based education (Robinson, 2024) to identifying barriers to parental involvement in early childhood education (Wildmon et al., 2024) or beginning teachers’struggles in regards to students’ and their own social-emotional development and needs (Martin, 2024), the papers in this issue underscore the importance of ensuring equitable access to quality education for all learners. Through rigorous inquiry, the authors shed light on the challenges faced by marginalized communities and advocate for inclusive practices that empower every student.

Another prominent theme that permeates the research presented here is the need for adaptability and resilience in education. Whether it is navigating the transformation of courses between different modalities in higher education (Bernauer et al., 2024) or responding to the disruptions caused by the COVID-19 pandemic (Scheopner Torres & D’Souza, 2024), educators and institutions must be flexible and innovative to meet learners' evolving needs, which are changing rapidly due to broader societal demands (e.g., Caddy & Sandilands, 2019).The papers in this issue provide valuable insights that can help in building resilient educational systems capable of withstanding 21st-century challenges and re-emphasize the importance of communities, both those of practice and local, in shaping the experiences of teachers and students. 

As lead editors, we extend our gratitude to the authors for their dedication to advancing knowledge in the field of education. We also express appreciation to the reviewers and editorial team for their meticulous attention to detail and commitment to academic excellence.

We invite you to immerse yourself in the rich tapestry of research presented in this issue, engage with the findings and insights, and join us in the ongoing dialogue surrounding the future of education. Together, let us work towards building a more equitable, resilient, and inclusive educational landscape for generations to come.

Warm regards,

Tipsuda Chaomuangkhong and Bregje van Geffen

Lead Editors of Current Issues in Education

References:

Bernauer, J.A., Fuller, R.G., & Cassels, A.M. (2024). Transforming courses across teaching modalities in higher education. Current Issues in Education, 25 (1). https://doi.org/10.14507/cie.vol25iss1.2157

Caddy, J., & Sandilands, R. (2019). Analytical Framework for Case Study Collection Effective Learning Environments . OECD.

Martin, P.C. (2024). Teacher SEL Space: Addressing Beginning Teachers’ Social Emotionalm Learning in a Support Group Structure. Current Issues in Education, 25 (3). https://doi.org/10.14507 /cie.vol25iss1.2186

Robinson, S. R. (2024). Hip Hop, social reproduction, and the possible selves of young Black men. Current Issues in Education, 25 (1).   https://doi.org/10.14507/cie.vol25iss1.2143

Scheopner Torres, A., & D’Souza, L. A. (2024). Pipeline disruption: The impact of COVID-19 on the next generation of teachers. Current Issues in Education, 25 (1). https://doi.org/10.14507/cie.vol25iss1.2125

Wildmon, M.E., Anthony, K.V., & Kamau, Z.J. (2024). Identifying and navigating the barriers of parental involvement in early childhood education. Current Issues in Education, 25 (1).  https://doi.org/10.14507/cie.vol25iss1.2146

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• Published: 16 June 2020

COVID-19 impact on research, lessons learned from COVID-19 research, implications for pediatric research

• Debra L. Weiner 1 , 2 ,
• Vivek Balasubramaniam 3 ,
• Shetal I. Shah 4 &
• Joyce R. Javier 5 , 6

on behalf of the Pediatric Policy Council

Pediatric Research volume  88 ,  pages 148–150 ( 2020 ) Cite this article

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The COVID-19 pandemic has resulted in unprecedented research worldwide. The impact on research in progress at the time of the pandemic, the importance and challenges of real-time pandemic research, and the importance of a pediatrician-scientist workforce are all highlighted by this epic pandemic. As we navigate through and beyond this pandemic, which will have a long-lasting impact on our world, including research and the biomedical research enterprise, it is important to recognize and address opportunities and strategies for, and challenges of research and strengthening the pediatrician-scientist workforce.

The first cases of what is now recognized as SARS-CoV-2 infection, termed COVID-19, were reported in Wuhan, China in December 2019 as cases of fatal pneumonia. By February 26, 2020, COVID-19 had been reported on all continents except Antarctica. As of May 4, 2020, 3.53 million cases and 248,169 deaths have been reported from 210 countries. 1

Impact of COVID-19 on ongoing research

The impact on research in progress prior to COVID-19 was rapid, dramatic, and no doubt will be long term. The pandemic curtailed most academic, industry, and government basic science and clinical research, or redirected research to COVID-19. Most clinical trials, except those testing life-saving therapies, have been paused, and most continuing trials are now closed to new enrollment. Ongoing clinical trials have been modified to enable home administration of treatment and virtual monitoring to minimize participant risk of COVID-19 infection, and to avoid diverting healthcare resources from pandemic response. In addition to short- and long-term patient impact, these research disruptions threaten the careers of physician-scientists, many of whom have had to shift efforts from research to patient care. To protect research in progress, as well as physician-scientist careers and the research workforce, ongoing support is critical. NIH ( https://grants.nih.gov/policy/natural-disasters/corona-virus.htm ), PCORI ( https://www.pcori.org/funding-opportunities/applicant-and-awardee-faqs-related-covid-19 ), and other funders acted swiftly to provide guidance on proposal submission and award management, and implement allowances that enable grant personnel to be paid and time lines to be relaxed. Research institutions have also implemented strategies to mitigate the long-term impact of research disruptions. Support throughout and beyond the pandemic to retain currently well-trained research personnel and research support teams, and to accommodate loss of research assets, including laboratory supplies and study participants, will be required to complete disrupted research and ultimately enable new research.

In the long term, it is likely that the pandemic will force reallocation of research dollars at the expense of research areas funded prior to the pandemic. It will be more important than ever for the pediatric research community to engage in discussion and decisions regarding prioritization of funding goals for dedicated pediatric research and meaningful inclusion of children in studies. The recently released 2020 National Institute of Child Health and Development (NICHD) strategic plan that engaged stakeholders, including scientists and patients, to shape the goals of the Institute, will require modification to best chart a path toward restoring normalcy within pediatric science.

COVID-19 research

This global pandemic once again highlights the importance of research, stable research infrastructure, and funding for public health emergency (PHE)/disaster preparedness, response, and resiliency. The stakes in this worldwide pandemic have never been higher as lives are lost, economies falter, and life has radically changed. Ultimate COVID-19 mitigation and crisis resolution is dependent on high-quality research aligned with top priority societal goals that yields trustworthy data and actionable information. While the highest priority goals are treatment and prevention, biomedical research also provides data critical to manage and restore economic and social welfare.

Scientific and technological knowledge and resources have never been greater and have been leveraged globally to perform COVID-19 research at warp speed. The number of studies related to COVID-19 increases daily, the scope and magnitude of engagement is stunning, and the extent of global collaboration unprecedented. On January 5, 2020, just weeks after the first cases of illness were reported, the genetic sequence, which identified the pathogen as a novel coronavirus, SARS-CoV-2, was released, providing information essential for identifying and developing treatments, vaccines, and diagnostics. As of May 3, 2020 1133 COVID-19 studies, including 148 related to hydroxychloroquine, 13 to remdesivir, 50 to vaccines, and 100 to diagnostic testing, were registered on ClinicalTrials.gov, and 980 different studies on the World Health Organization’s International Clinical Trials Registry Platform (WHO ICTRP), made possible, at least in part, by use of data libraries to inform development of antivirals, immunomodulators, antibody-based biologics, and vaccines. On April 7, 2020, the FDA launched the Coronavirus Treatment Acceleration Program (CTAP) ( https://www.fda.gov/drugs/coronavirus-covid-19-drugs/coronavirus-treatment-acceleration-program-ctap ). On April 17, 2020, NIH announced a partnership with industry to expedite vaccine development ( https://www.nih.gov/news-events/news-releases/nih-launch-public-private-partnership-speed-covid-19-vaccine-treatment-options ). As of May 1, 2020, remdesivir (Gilead), granted FDA emergency use authorization, is the only approved therapeutic for COVID-19. 2

The pandemic has intensified research challenges. In a rush for data already thousands of manuscripts, news reports, and blogs have been published, but to date, there is limited scientifically robust data. Some studies do not meet published clinical trial standards, which now include FDA’s COVID-19-specific standards, 3 , 4 , 5 and/or are published without peer review. Misinformation from studies diverts resources from development and testing of more promising therapeutic candidates and has endangered lives. Ibuprofen, initially reported as unsafe for patients with COVID-19, resulted in a shortage of acetaminophen, endangering individuals for whom ibuprofen is contraindicated. Hydroxychloroquine initially reported as potentially effective for treatment of COVID-19 resulted in shortages for patients with autoimmune diseases. Remdesivir, in rigorous trials, showed decrease in duration of COVID-19, with greater effect given early. 6 Given the limited availability and safety data, the use outside clinical trials is currently approved only for severe disease. Vaccines typically take 10–15 years to develop. As of May 3, 2020, of nearly 100 vaccines in development, 8 are in trial. Several vaccines are projected to have emergency approval within 12–18 months, possibly as early as the end of the year, 7 still an eternity for this pandemic, yet too soon for long-term effectiveness and safety data. Antibody testing, necessary for diagnosis, therapeutics, and vaccine testing, has presented some of the greatest research challenges, including validation, timing, availability and prioritization of testing, interpretation of test results, and appropriate patient and societal actions based on results. 8 Relaxing physical distancing without data regarding test validity, duration, and strength of immunity to different strains of COVID-19 could have catastrophic results. Understanding population differences and disparities, which have been further exposed during this pandemic, is critical for response and long-term pandemic recovery. The “Equitable Data Collection and Disclosure on COVID-19 Act” calls for the CDC (Centers for Disease Control and Prevention) and other HHS (United States Department of Health & Human Services) agencies to publicly release racial and demographic information ( https://bass.house.gov/sites/bass.house.gov/files/Equitable%20Data%20Collection%20and%20Dislosure%20on%20COVID19%20Act_FINAL.pdf )

Trusted sources of up-to-date, easily accessible information must be identified (e.g., WHO https://www.who.int/emergencies/diseases/novel-coronavirus-2019/global-research-on-novel-coronavirus-2019-ncov , CDC https://www.cdc.gov/coronavirus/2019-nCoV/hcp/index.html , and for children AAP (American Academy of Pediatrics) https://www.aappublications.org/cc/covid-19 ) and should comment on quality of data and provide strategies and crisis standards to guide clinical practice.

Long-term, lessons learned from research during this pandemic could benefit the research enterprise worldwide beyond the pandemic and during other PHE/disasters with strategies for balancing multiple novel approaches and high-quality, time-efficient, cost-effective research. This challenge, at least in part, can be met by appropriate study design, collaboration, patient registries, automated data collection, artificial intelligence, data sharing, and ongoing consideration of appropriate regulatory approval processes. In addition, research to develop and evaluate innovative strategies and technologies to improve access to care, management of health and disease, and quality, safety, and cost effectiveness of care could revolutionize healthcare and healthcare systems. During PHE/disasters, crisis standards for research should be considered along with ongoing and just-in-time PHE/disaster training for researchers willing to share information that could be leveraged at time of crisis. A dedicated funded core workforce of PHE/disaster researchers and funded infrastructure should be considered, potentially as a consortium of networks, that includes physician-scientists, basic scientists, social scientists, mental health providers, global health experts, epidemiologists, public health experts, engineers, information technology experts, economists and educators to strategize, consult, review, monitor, interpret studies, guide appropriate clinical use of data, and inform decisions regarding effective use of resources for PHE/disaster research.

Differences between adult and pediatric COVID-19, the need for pediatric research

As reported by the CDC, from February 12 to April 2, 2020, of 149,760 cases of confirmed COVID-19 in the United States, 2572 (1.7%) were children aged <18 years, similar to published rates in China. 9 Severe illness has been rare. Of 749 children for whom hospitalization data is available, 147 (20%) required hospitalization (5.7% of total children), and 15 of 147 required ICU care (2.0%, 0.58% of total). Of the 95 children aged <1 year, 59 (62%) were hospitalized, and 5 (5.3%) required ICU admission. Among children there were three deaths. Despite children being relatively spared by COVID-19, spread of disease by children, and consequences for their health and pediatric healthcare are potentially profound with immediate and long-term impact on all of society.

We have long been aware of the importance and value of pediatric research on children, and society. COVID-19 is no exception and highlights the imperative need for a pediatrician-scientist workforce. Understanding differences in epidemiology, susceptibility, manifestations, and treatment of COVID-19 in children can provide insights into this pathogen, pathogen–host interactions, pathophysiology, and host response for the entire population. Pediatric clinical registries of COVID-infected, COVID-exposed children can provide data and specimens for immediate and long-term research. Of the 1133 COVID-19 studies on ClinicalTrials.gov, 202 include children aged ≤17 years. Sixty-one of the 681 interventional trials include children. With less diagnostic testing and less pediatric research, we not only endanger children, but also adults by not identifying infected children and limiting spread by children.

Pediatric considerations and challenges related to treatment and vaccine research for COVID-19 include appropriate dosing, pediatric formulation, and pediatric specific short- and long-term effectiveness and safety. Typically, initial clinical trials exclude children until safety has been established in adults. But with time of the essence, deferring pediatric research risks the health of children, particularly those with special needs. Considerations specific to pregnant women, fetuses, and neonates must also be addressed. Childhood mental health in this demographic, already struggling with a mental health pandemic prior to COVID-19, is now further challenged by social disruption, food and housing insecurity, loss of loved ones, isolation from friends and family, and exposure to an infodemic of pandemic-related information. Interestingly, at present mental health visits along with all visits to pediatric emergency departments across the United States are dramatically decreased. Understanding factors that mitigate and worsen psychiatric symptoms should be a focus of research, and ideally will result in strategies for prevention and management in the long term, including beyond this pandemic. Social well-being of children must also be studied. Experts note that the pandemic is a perfect storm for child maltreatment given that vulnerable families are now socially isolated, facing unemployment, and stressed, and that children are not under the watch of mandated reporters in schools, daycare, and primary care. 10 Many states have observed a decrease in child abuse reports and an increase in severity of emergency department abuse cases. In the short term and long term, it will be important to study the impact of access to care, missed care, and disrupted education during COVID-19 on physical and cognitive development.

Training and supporting pediatrician-scientists, such as through NIH physician-scientist research training and career development programs ( https://researchtraining.nih.gov/infographics/physician-scientist ) at all stages of career, as well as fostering research for fellows, residents, and medical students willing to dedicate their research career to, or at least understand implications of their research for, PHE/disasters is important for having an ongoing, as well as a just-in-time surge pediatric-focused PHE/disaster workforce. In addition to including pediatric experts in collaborations and consortiums with broader population focus, consideration should be given to pediatric-focused multi-institutional, academic, industry, and/or government consortiums with infrastructure and ongoing funding for virtual training programs, research teams, and multidisciplinary oversight.

The impact of the COVID-19 pandemic on research and research in response to the pandemic once again highlights the importance of research, challenges of research particularly during PHE/disasters, and opportunities and resources for making research more efficient and cost effective. New paradigms and models for research will hopefully emerge from this pandemic. The importance of building sustained PHE/disaster research infrastructure and a research workforce that includes training and funding for pediatrician-scientists and integrates the pediatrician research workforce into high-quality research across demographics, supports the pediatrician-scientist workforce and pipeline, and benefits society.

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Department of Pediatrics, Division of Emergency Medicine, Boston Children’s Hospital, Boston, MA, USA

Debra L. Weiner

Harvard Medical School, Boston, MA, USA

Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA

Vivek Balasubramaniam

Department of Pediatrics and Division of Neonatology, Maria Fareri Children’s Hospital at Westchester Medical Center, New York Medical College, Valhalla, NY, USA

Shetal I. Shah

Division of General Pediatrics, Children’s Hospital Los Angeles, Los Angeles, CA, USA

Joyce R. Javier

Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

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All authors made substantial contributions to conception and design, data acquisition and interpretation, drafting the manuscript, and providing critical revisions. All authors approve this final version of the manuscript.

Pediatric Policy Council

Scott C. Denne, MD, Chair, Pediatric Policy Council; Mona Patel, MD, Representative to the PPC from the Academic Pediatric Association; Jean L. Raphael, MD, MPH, Representative to the PPC from the Academic Pediatric Association; Jonathan Davis, MD, Representative to the PPC from the American Pediatric Society; DeWayne Pursley, MD, MPH, Representative to the PPC from the American Pediatric Society; Tina Cheng, MD, MPH, Representative to the PPC from the Association of Medical School Pediatric Department Chairs; Michael Artman, MD, Representative to the PPC from the Association of Medical School Pediatric Department Chairs; Shetal Shah, MD, Representative to the PPC from the Society for Pediatric Research; Joyce Javier, MD, MPH, MS, Representative to the PPC from the Society for Pediatric Research.

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Weiner, D.L., Balasubramaniam, V., Shah, S.I. et al. COVID-19 impact on research, lessons learned from COVID-19 research, implications for pediatric research. Pediatr Res 88 , 148–150 (2020). https://doi.org/10.1038/s41390-020-1006-3

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Issue Date : August 2020

DOI : https://doi.org/10.1038/s41390-020-1006-3

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Some graduation requirements waived for WA’s class of 2024

Washington school districts will be able to keep waiving certain graduation requirements for high school students in the class of 2024. 

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